Valence & Arousal: Analysis
Set-Up
Note, DecTools mascks ICC and SD from the psych package.
#Packages
library(tidyverse) # data manipulation
library(psych) # data descriptives
library(DescTools) # Lin's Correspondance Coefficient
library(gt) # table formatting
library(gtable) # table formatting
library(gtsummary) # table summaries
library(kableExtra) # table formatting
library(knitr) # html table formatting
library(irr) # interrater reliabilities
library(stringr) # work with strings
library(labelled) # work with labels
library(lubridate) # date formatting
library(viridis) # color pallets
library(plotly) # 3D plots
#Set gt_theme for summary tables
theme_gtsummary_compact()Import Files
Note on the OASIS file:
The OASIS study (Kurdi et al., 2017), human participants rated the images on a 1-7 likert scale. All subsequent machine and human ratings were done on a 1-9 likert scale.
The variables are denoted as follows:
“actual” OASIS vars (actual_valence & actual_arousal) were the participant rated OASIS images on a 1-7 scale.
“adjusted” OASIS values (adjusted_valence & adjusted_arousal) are the participant values adjusted to a 1-9 scale.
Kurdi, B., Lozano, S., & Banaji, M. R. (2017). Introducing the Open Affective Standardized Image Set (OASIS). Behavior Research Methods, 49(2), 457–470. https://doi.org/10.3758/s13428-016-0715-3
#Human Data
image_ratings_human_url <- "https://raw.githubusercontent.com/The-Change-Lab/affectivedynamics/main/data/image_ratings_human.csv"
image_ratings_human <- read.csv(file=url(image_ratings_human_url), header=T)
#All Ratings
image_ratings_all_url <- "https://raw.githubusercontent.com/The-Change-Lab/affectivedynamics/main/data/image_ratings_all.csv"
image_ratings_all <- read.csv(file=url(image_ratings_all_url), header=T)
#Day in the Life Deep Affect Modules (DAM)
image_ratings_DITL_url <- "https://raw.githubusercontent.com/The-Change-Lab/affectivedynamics/main/data/image_ratings_DITL.csv"
image_ratings_DITL <- read.csv(file=url(image_ratings_DITL_url), header=T)Reshape & Subset Files
Resphaping and subsetting files for later plotting & analysis.
#Ratings by source
image_ratings_smartphone <- image_ratings_all %>%
filter(source == "smartphone")
image_ratings_OASIS <- image_ratings_all %>%
filter(source == "OASIS")
#Long form valence ratings
valence_long <- image_ratings_all %>%
select(image, valence_human, valence_ml, source) %>%
pivot_longer(cols = c("valence_human", "valence_ml"),
names_to = "raiter",
values_to = "valence") %>%
mutate(raiter = ifelse(raiter == "valence_human", "human", "machine"))
#Long form arousal ratings
arousal_long <- image_ratings_all %>%
select(image, arousal_human, arousal_ml, source) %>%
pivot_longer(cols = c("arousal_human", "arousal_ml"),
names_to = "raiter",
values_to = "arousal") %>%
mutate(raiter = ifelse(raiter == "arousal_human", "human", "machine"))
#Long form ratings
image_ratings_all_long <- merge(valence_long, arousal_long, by = c("image", "raiter"))
image_ratings_all_long <- image_ratings_all_long %>%
rename(source = source.x) %>%
select(image, raiter, source, valence, arousal)
image_ratings_all_extralong <- image_ratings_all %>%
select(image, arousal_human, arousal_ml, valence_human, valence_ml, source) %>%
pivot_longer(cols = c("valence_human", "valence_ml", "arousal_human", "arousal_ml"),
names_to = "raiter",
values_to = "value") %>%
mutate(measure = gsub("_.*", "", raiter),
raiter = gsub(".*_", "", raiter))
#DITL Label Images
image_ratings_DITL <- image_ratings_DITL %>%
arrange(date) %>%
mutate(row_id=row_number())
#DITL Long
image_ratings_DITL_long <- image_ratings_DITL %>%
pivot_longer(cols = c("valence", "arousal"),
names_to = "rating")Data Overview
Source
#Source
image_ratings_human %>%
mutate(source = as_factor(source)) %>%
select(source) %>%
tbl_summary(
label = list(source ~ "Source"),
statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Valence & Arousal Survey Data") | Valence & Arousal Survey Data | |
| N = 8321 | |
|---|---|
| Source | |
| student | 381 (46%) |
| prolific | 451 (54%) |
| 1 n (%) | |
Demographics
#Basic Demographics
image_ratings_human %>%
mutate(gender = factor(gender,
levels=c(1,2,3,4,5),
labels=c("Male", "Female", "Non-binary", "Other",
"Prefer not to answer")),
race = factor(race,
levels=c(1,2,3,4,5,6,7),
labels=c("White", "Native American", "Asian", "Black", "Other",
"Prefer not to answer", "Multiracial")),
hispanic = factor(hispanic,
levels=c(1,2,3),
labels=c("Hispanic or Latino/Latina",
"Not Hispanic or Latino/Latina",
"I prefer not to answer"))) %>%
select(age, gender, race, hispanic) %>%
tbl_summary(
label = list(age ~"Age", gender ~ "Gender", race ~ "Race", hispanic ~ "Hispanic"),
statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Valence & Arousal Survey Data") | Valence & Arousal Survey Data | |
| N = 8321 | |
|---|---|
| Age | 29 (18, 75) |
| Gender | |
| Male | 374 (45%) |
| Female | 443 (53%) |
| Non-binary | 6 (0.7%) |
| Other | 5 (0.6%) |
| Prefer not to answer | 4 (0.5%) |
| Race | |
| White | 478 (58%) |
| Native American | 8 (1.0%) |
| Asian | 163 (20%) |
| Black | 59 (7.1%) |
| Other | 53 (6.4%) |
| Prefer not to answer | 22 (2.6%) |
| Multiracial | 48 (5.8%) |
| Unknown | 1 |
| Hispanic | |
| Hispanic or Latino/Latina | 134 (16%) |
| Not Hispanic or Latino/Latina | 673 (81%) |
| I prefer not to answer | 25 (3.0%) |
| 1 Mean (Range); n (%) | |
#Expanded Demographics
image_ratings_human %>%
mutate(education = factor(education,
levels=c(1,2,3,4,5,6,7,8),
labels=c("Less than high school", "Some college",
"2-year college degree", "4-year college degree",
"Master's degree", "Doctoral's degree",
"Professional degree, e.g., JD/MD",
"High school/GED/Technical/vocational training)")),
marital_status = factor(marital_status,
levels=c(1,2,3,4),
labels=c("Married, or living as married",
"Divorced or separated",
"Widowed",
"Single/never married")),
smartphone_hours = factor(smartphone_hours,
levels=c(1,2,3,4,5),
labels=c("0-1 hours", "1-3 hours",
"3-5 hours", "5-7 hours",
"More than 7 hours"))) %>%
select(education, marital_status, smartphone_hours) %>%
tbl_summary(
label = list(education ~ "Education",
marital_status ~ "Marital Status",
smartphone_hours ~ "Smartphone Hours"),
statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Valence & Arousal Survey Data") | Valence & Arousal Survey Data | |
| N = 8321 | |
|---|---|
| Education | |
| Less than high school | 4 (0.5%) |
| Some college | 356 (43%) |
| 2-year college degree | 59 (7.1%) |
| 4-year college degree | 188 (23%) |
| Master's degree | 52 (6.3%) |
| Doctoral's degree | 6 (0.7%) |
| Professional degree, e.g., JD/MD | 11 (1.3%) |
| High school/GED/Technical/vocational training) | 156 (19%) |
| Marital Status | |
| Married, or living as married | 169 (20%) |
| Divorced or separated | 40 (4.8%) |
| Widowed | 1 (0.1%) |
| Single/never married | 622 (75%) |
| Smartphone Hours | |
| 0-1 hours | 42 (5.0%) |
| 1-3 hours | 261 (31%) |
| 3-5 hours | 294 (35%) |
| 5-7 hours | 170 (20%) |
| More than 7 hours | 65 (7.8%) |
| 1 n (%) | |
Polific Only Demographics
#Prolific Only Demographics
image_ratings_human %>%
filter(source == "prolific") %>%
mutate(income = factor(income,
levels=c(1,2,3,4,5,6,7,8,9,10,11,12),
labels=c("$14,999 or less", "$15,000 - $24,999",
"$25,000 - $29,999","$30,000 - $34,999",
"$35,000 - $49,999", "$50,000 - $74,999",
"$75,000 - $99,999", "$100,000 - $149,999",
"$150,000 - $199,999", "$200,000 or more",
"Don't know", "I prefer not to answer")),
marital_status = factor(marital_status,
levels=c(1,2,3,4),
labels=c("Married, or living as married",
"Divorced or separated",
"Widowed",
"Single/never married")),
residence = factor(residence,
levels=c(1,2,3),
labels=c("Urban", "Suburban", "Rural")),
region = factor(region,
levels=c(1,2,3,4),
labels=c("Northeast", "South", "West", "Midwest"))) %>%
select(marital_status, income, residence, region) %>%
tbl_summary(
label = list(income ~ "Income",
marital_status ~ "Marital Status",
residence ~ "Residence", region ~ "Region"),
statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Valence & Arousal Survey Data") | Valence & Arousal Survey Data | |
| N = 4511 | |
|---|---|
| Marital Status | |
| Married, or living as married | 155 (34%) |
| Divorced or separated | 34 (7.5%) |
| Widowed | 0 (0%) |
| Single/never married | 262 (58%) |
| Income | |
| $14,999 or less | 44 (9.8%) |
| $15,000 - $24,999 | 28 (6.2%) |
| $25,000 - $29,999 | 24 (5.3%) |
| $30,000 - $34,999 | 27 (6.0%) |
| $35,000 - $49,999 | 60 (13%) |
| $50,000 - $74,999 | 88 (20%) |
| $75,000 - $99,999 | 61 (14%) |
| $100,000 - $149,999 | 64 (14%) |
| $150,000 - $199,999 | 26 (5.8%) |
| $200,000 or more | 15 (3.3%) |
| Don't know | 6 (1.3%) |
| I prefer not to answer | 8 (1.8%) |
| Residence | |
| Urban | 139 (31%) |
| Suburban | 252 (56%) |
| Rural | 60 (13%) |
| Region | |
| Northeast | 92 (20%) |
| South | 150 (33%) |
| West | 116 (26%) |
| Midwest | 93 (21%) |
| 1 n (%) | |
Location
#All students were attending suburban, west coast institutions
image_ratings_human <- image_ratings_human %>%
mutate(residence_all = ifelse(source == "prolific", residence, 2),
region_all = ifelse(source == "prolific", region, 3))
#Label factor levels
residence_relabel <- function(orig_data){
val_labels(orig_data) <-
c("Urban" = 1,
"Suburban" = 2,
"Rural" = 3
)
return(orig_data)
}
region_relabel <- function(orig_data){
val_labels(orig_data) <-
c("Northeast" = 1,
"South" = 2,
"West" = 3,
"Midwest" = 4
)
return(orig_data)
}
image_ratings_human$residence_all <- residence_relabel(image_ratings_human$residence_all)
image_ratings_human$region_all <- region_relabel(image_ratings_human$region_all)
#Look at location summary
image_ratings_human %>%
mutate(residence_all = as_factor(residence_all),
region_all = as_factor(region_all)) %>%
select(residence_all, region_all) %>%
tbl_summary(
label = list(residence_all ~ "Residence", region_all ~ "Region"),
statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Valence & Arousal Survey Data") | Valence & Arousal Survey Data | |
| N = 8321 | |
|---|---|
| Residence | |
| Urban | 139 (17%) |
| Suburban | 633 (76%) |
| Rural | 60 (7.2%) |
| Region | |
| Northeast | 92 (11%) |
| South | 150 (18%) |
| West | 497 (60%) |
| Midwest | 93 (11%) |
| 1 n (%) | |
Valence & Arousal Plots
Valence Plots
#Valence Ratings
image_ratings_all %>%
ggplot(aes(x = valence_human, y = valence_ml)) +
geom_smooth(method = "lm", se = T, colour = "grey", linewidth = 0.5, alpha = 0.5) +
geom_point(colour = "blue") +
ggtitle("Valence") +
scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("ML Ratings", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) image_ratings_all %>%
ggplot(aes(x = valence_human, y = valence_ml, color = source)) +
geom_point() +
geom_smooth(method = "lm") +
ggtitle("Valence") +
scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) Arousal Plots
#Arousal Ratings
image_ratings_all %>%
ggplot(aes(x = arousal_human, y = arousal_ml)) +
geom_smooth(method = "lm", se = T, colour = "grey", linewidth = 0.5, alpha = 0.5) +
geom_point(colour = "red") +
ggtitle("Arousal") +
scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))image_ratings_all %>%
ggplot(aes(x = arousal_human, y = arousal_ml, color = source)) +
geom_point() +
geom_smooth(method = "lm") +
ggtitle("Arousal") +
scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))Comparing Valence & Arousal
#All by Rater
image_ratings_all_long %>%
group_by(image) %>%
ggplot(aes(x = arousal, y = valence, color = raiter)) +
geom_point() +
geom_line(aes(group = image), color="grey") +
ggtitle("Valence and Arousal by Raiter") +
scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))image_ratings_all_long %>%
group_by(image) %>%
ggplot(aes(x = arousal, y = valence, color = raiter)) +
geom_point() +
geom_line(aes(group = image), color="grey") +
ggtitle("Valence and Arousal by Raiter") +
scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
facet_wrap(~source)By Image Source
#Arousal
image_ratings_all %>%
group_by(source) %>%
ggplot(aes(x = arousal_human, y = arousal_ml)) +
geom_point() +
geom_smooth(method = "lm") +
ggtitle("Arousal") +
scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
facet_wrap(~source)#Valence
image_ratings_all %>%
group_by(source) %>%
ggplot(aes(x = valence_human, y = valence_ml)) +
geom_point() +
geom_smooth(method = "lm") +
ggtitle("Valence") +
scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
facet_wrap(~source)#Valence and Arousal
image_ratings_all_long %>%
group_by(source) %>%
ggplot(aes(x = arousal, y = valence)) +
geom_point() +
geom_smooth(method = "lm") +
ggtitle("Rating by Source and Raiter") +
scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
facet_wrap(~source + raiter)Av Participant Rating Across Images
Plot showing average participant ratings on valence and arousal across all images.
#Valence
image_ratings_all %>%
group_by(source) %>%
ggplot() +
geom_point(mapping = aes(x = reorder(image, valence_human),
y = valence_human, color = source)) +
ggtitle("Average Participant Valence Ratings Across Images") +
xlab("Image") +
scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank())#Arousal
image_ratings_all %>%
group_by(source) %>%
ggplot() +
geom_point(mapping = aes(x = reorder(image, arousal_human),
y = arousal_human, color = source)) +
ggtitle("Average Participant Arousal Ratings Across Images") +
xlab("Image") +
scale_y_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank())#Faceted
image_ratings_all_extralong %>%
group_by(source) %>%
ggplot() +
geom_point(mapping = aes(x = reorder(image, value),
y = value, color = source)) +
ggtitle("Average Participant Ratings Across Images") +
xlab("Image") +
scale_y_continuous("Rating", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
facet_wrap(~measure)Smartphone with Elipses
#All by Rater
image_ratings_all_long %>%
filter(source == "smartphone") %>%
mutate(Raiter = ifelse(raiter == "human", "Human", "Machine")) %>%
group_by(image) %>%
ggplot(aes(x = arousal, y = valence, color = Raiter)) +
geom_point(linewidth = 1.5) +
scale_colour_manual("Ratings", values =
c("Human"="#1f77b4", "Machine"="#ff7f0e")) +
stat_ellipse(linewidth = 1) +
geom_line(aes(group = image), color="black", size = 0.2) +
ggtitle("Valence and Arousal:\nSmartphone Images\n") +
scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"),
text = element_text(size = 16))Triangle Plot: OASIS
Plot displaying distance between participant, ML, and previous OASIS ratings on OASIS images.
image_ratings_all %>%
filter(source=="OASIS")%>%
ggplot() +
geom_point(mapping = aes(x = arousal_human, y = valence_human,
colour = "Human"), size = 2) +
stat_ellipse(mapping = aes(x = arousal_human, y = valence_human,
colour = "Participant"), size = 1) +
geom_point(mapping = aes(x = arousal_ml, y = valence_ml,
colour = "Human"), size = 2) +
stat_ellipse(mapping = aes(x = arousal_ml, y = valence_ml,
colour = "Machine"), size = 1) +
geom_point(mapping = aes(x = adjusted_arousal, y = adjusted_valence,
colour = "Prior OASIS"), size = 2) +
stat_ellipse(mapping = aes(x = adjusted_arousal, y = adjusted_valence,
colour = "Prior OASIS"), size = 1) +
scale_colour_manual("Ratings", values =
c("Human"="#1f77b4",
"Machine"="#ff7f0e",
"Prior OASIS"="#2ca02c")) +
geom_segment(aes(x = arousal_human, y = valence_human,
xend= arousal_ml, yend = valence_ml),
linetype = 1, size = 0.5, color ="black") +
geom_segment(aes(x = arousal_ml, y = valence_ml,
xend = adjusted_arousal, yend = adjusted_valence),
linetype = 1, size = 0.5, color ="black") +
geom_segment(aes(x = adjusted_arousal, y = adjusted_valence,
xend= arousal_human, yend = valence_human),
linetype = 1, size = 0.5, color ="black") +
scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
scale_y_continuous("Valence", limits = c(-1,10), n.breaks = 11) +
ggtitle("Participant, ML, and Previous \nOASIS Rating Relationships") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"),
text = element_text(size = 16))Valence & Arousal Data
Data Overview
## Rows: 832
## Columns: 221
## $ pid <int> 1, 10, 100, 101, 102, 103, 104, 105, 106, 107, 108, 1…
## $ source <chr> "student", "student", "prolific", "student", "student…
## $ age <int> 19, 36, 31, 28, 20, 54, 28, 20, 22, 19, 19, 36, 28, 2…
## $ race <int> 6, 3, 1, 1, 1, 1, 1, 1, 1, 7, 4, 1, 1, 4, 1, 5, 1, 3,…
## $ native <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ asian <int> NA, 1, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA,…
## $ black <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 1,…
## $ pacific_islander <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ white <int> NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, NA, 1, NA, …
## $ other_race <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ pnta_race <int> 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hispanic <int> 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2,…
## $ gender <int> 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2,…
## $ residence <int> NA, NA, 1, NA, NA, 2, NA, 2, NA, NA, NA, 2, 1, 1, 2, …
## $ region <int> NA, NA, 2, NA, NA, 2, NA, 1, NA, NA, NA, 4, 4, 4, 1, …
## $ education <int> 8, 3, 4, 3, 2, 4, 2, 2, 4, 2, 2, 4, 4, 8, 2, 2, 4, 4,…
## $ marital_status <int> 4, 4, 1, 4, 4, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,…
## $ income <int> NA, NA, 5, NA, NA, 9, NA, 8, NA, NA, NA, 7, 6, 1, 1, …
## $ smartphone_hours <int> 5, 2, 3, 3, 3, 2, 2, 2, 5, 2, 3, 1, 4, 1, 3, 4, 2, 3,…
## $ valence_1 <int> NA, 4, NA, 2, 3, NA, NA, 1, 3, 1, NA, NA, 2, 5, 4, 4,…
## $ valence_2 <int> NA, 4, NA, 4, 2, NA, NA, 3, 2, 3, NA, NA, 5, 5, 4, 6,…
## $ valence_3 <int> NA, 6, NA, 9, 6, NA, NA, 9, 5, 8, NA, NA, 5, 8, 9, 7,…
## $ valence_4 <int> NA, 6, NA, 8, 7, NA, NA, 7, 5, 4, NA, NA, 3, 5, 9, 6,…
## $ valence_5 <int> NA, 4, NA, 2, 5, NA, NA, 4, 4, 4, NA, NA, 2, 3, 3, 5,…
## $ valence_6 <int> NA, 5, NA, 4, 3, NA, NA, 5, 5, 4, NA, NA, 5, 5, 4, 5,…
## $ valence_7 <int> NA, 5, NA, 5, 4, NA, NA, 5, 4, 5, NA, NA, 5, 4, 4, 5,…
## $ valence_8 <int> NA, 5, NA, 5, 1, NA, NA, 5, 5, 5, NA, NA, 2, 5, 4, 5,…
## $ valence_9 <int> NA, 5, NA, 4, 3, NA, NA, 5, 5, 4, NA, NA, 5, 5, 4, 5,…
## $ valence_10 <int> NA, 6, NA, 6, 7, NA, NA, 6, 6, 6, NA, NA, 5, 6, 4, 6,…
## $ valence_11 <int> NA, 7, NA, 7, 6, NA, NA, 5, 6, 5, NA, NA, 5, 6, 9, 5,…
## $ valence_12 <int> NA, 6, NA, 5, 6, NA, NA, 5, 5, 3, NA, NA, 6, 5, 5, 5,…
## $ valence_13 <int> NA, 1, NA, 3, 2, NA, NA, 5, 8, 2, NA, NA, 5, 7, 1, 5,…
## $ valence_14 <int> NA, 2, NA, 2, 1, NA, NA, 6, 6, 1, NA, NA, 4, 7, 1, 5,…
## $ valence_15 <int> NA, 4, NA, 5, 2, NA, NA, 5, 8, 1, NA, NA, 3, 6, 5, 5,…
## $ valence_16 <int> NA, 4, NA, 6, 2, NA, NA, 6, 6, 3, NA, NA, 5, 6, 3, 5,…
## $ valence_17 <int> NA, 3, NA, 2, 1, NA, NA, 1, 3, 1, NA, NA, 1, 2, 1, 2,…
## $ valence_18 <int> NA, 3, NA, 4, 2, NA, NA, 6, 9, 2, NA, NA, 4, 7, 1, 5,…
## $ valence_19 <int> NA, 6, NA, 7, 7, NA, NA, 6, 6, 6, NA, NA, 7, 6, 9, 6,…
## $ valence_20 <int> NA, 5, NA, 5, 3, NA, NA, 5, 3, 2, NA, NA, 4, 6, 4, 4,…
## $ valence_21 <int> NA, 6, NA, 6, 7, NA, NA, 7, 8, 7, NA, NA, 8, 7, 9, 7,…
## $ valence_22 <int> NA, 5, NA, 3, 5, NA, NA, 6, 9, 2, NA, NA, 9, 5, 5, 5,…
## $ valence_23 <int> NA, 4, NA, 5, 1, NA, NA, 1, 5, 5, NA, NA, 5, 3, 5, 5,…
## $ valence_24 <int> NA, 4, NA, 4, 5, NA, NA, 2, 4, 4, NA, NA, 2, 2, 4, 4,…
## $ valence_25 <int> NA, 4, NA, 3, 3, NA, NA, 3, 2, 3, NA, NA, 2, 5, 3, 2,…
## $ valence_26 <int> NA, 4, NA, 4, 5, NA, NA, 5, 3, 6, NA, NA, 5, 5, 5, 5,…
## $ valence_27 <int> NA, 5, NA, 5, 4, NA, NA, 5, 6, 6, NA, NA, 3, 5, 9, 5,…
## $ valence_28 <int> NA, 4, NA, 9, 8, NA, NA, 7, 6, 9, NA, NA, 6, 6, 9, 7,…
## $ valence_29 <int> NA, 2, NA, 1, 2, NA, NA, 1, 2, 1, NA, NA, 1, 2, 1, 2,…
## $ valence_30 <int> NA, 6, NA, 5, 6, NA, NA, 5, 5, 6, NA, NA, 5, 6, 5, 5,…
## $ valence_31 <int> NA, 5, NA, 4, 2, NA, NA, 5, 5, 3, NA, NA, 5, 3, 5, 5,…
## $ valence_32 <int> NA, 5, NA, 5, 6, NA, NA, 5, 5, 5, NA, NA, 5, 7, 5, 5,…
## $ valence_33 <int> NA, 3, NA, 4, 4, NA, NA, 4, 5, 1, NA, NA, 3, 4, 5, 4,…
## $ valence_34 <int> NA, 5, NA, 5, 4, NA, NA, 4, 6, 5, NA, NA, 2, 5, 4, 4,…
## $ valence_35 <int> NA, 5, NA, 5, 5, NA, NA, 5, 5, 5, NA, NA, 2, 6, 9, 7,…
## $ valence_36 <int> NA, 6, NA, 5, 5, NA, NA, 5, 5, 3, NA, NA, 5, 6, 9, 5,…
## $ valence_37 <int> NA, 6, NA, 7, 7, NA, NA, 8, 6, 5, NA, NA, 2, 7, 9, 7,…
## $ valence_38 <int> NA, 2, NA, 1, 2, NA, NA, 1, 2, 1, NA, NA, 1, 3, 1, 1,…
## $ valence_39 <int> NA, 2, NA, 1, 3, NA, NA, 2, 4, 4, NA, NA, 5, 2, 1, 2,…
## $ valence_40 <int> NA, 5, NA, 5, 5, NA, NA, 5, 5, 3, NA, NA, 5, 5, 5, 5,…
## $ valence_41 <int> NA, 1, NA, 2, 3, NA, NA, 5, 8, 1, NA, NA, 3, 8, 1, 5,…
## $ valence_42 <int> NA, 1, NA, 3, 3, NA, NA, 6, 5, 2, NA, NA, 2, 5, 3, 7,…
## $ valence_43 <int> NA, 1, NA, 2, 1, NA, NA, 5, 6, 1, NA, NA, 4, 8, 1, 5,…
## $ valence_44 <int> NA, 5, NA, 6, 6, NA, NA, 5, 5, 5, NA, NA, 5, 5, 5, 5,…
## $ valence_45 <int> NA, 3, NA, 5, 6, NA, NA, 2, 6, 5, NA, NA, 2, 3, 2, 7,…
## $ valence_46 <int> NA, 4, NA, 5, 6, NA, NA, 3, 6, 4, NA, NA, 1, 6, 2, 6,…
## $ valence_47 <int> NA, 4, NA, 4, 4, NA, NA, 4, 2, 6, NA, NA, 3, 5, 6, 4,…
## $ valence_48 <int> NA, 4, NA, 3, 3, NA, NA, 5, 4, 1, NA, NA, 3, 5, 5, 8,…
## $ valence_49 <int> NA, 4, NA, 6, 5, NA, NA, 5, 6, 2, NA, NA, 5, 5, 5, 5,…
## $ valence_50 <int> NA, 1, NA, 1, 1, NA, NA, 1, 2, 1, NA, NA, 3, 2, 1, 1,…
## $ valence_51 <int> 4, 4, 5, 7, NA, 9, 6, NA, 3, 9, 5, 6, NA, NA, 3, 4, 7…
## $ valence_52 <int> 9, 9, 8, 9, NA, 9, 9, NA, 8, 9, 9, 8, NA, NA, 4, 6, 9…
## $ valence_53 <int> 1, 1, 3, 1, NA, 1, 3, NA, 2, 1, 3, 3, NA, NA, 1, 3, 2…
## $ valence_54 <int> 4, 4, 4, 1, NA, 2, 8, NA, 2, 2, 6, 3, NA, NA, 2, 3, 4…
## $ valence_55 <int> 8, 5, 3, 1, NA, 3, 2, NA, 5, 4, 7, 5, NA, NA, 5, 5, 6…
## $ valence_56 <int> 4, 5, 4, 5, NA, 3, 5, NA, 5, 3, 3, 5, NA, NA, 5, 5, 5…
## $ valence_57 <int> 7, 6, 4, 1, NA, 5, 4, NA, 5, 6, 7, 5, NA, NA, 5, 5, 8…
## $ valence_58 <int> 8, 7, 5, 1, NA, 5, 8, NA, 6, 5, 6, 7, NA, NA, 5, 5, 7…
## $ valence_59 <int> 7, 7, 5, 5, NA, 9, 3, NA, 6, 2, 4, 6, NA, NA, 6, 7, 5…
## $ valence_60 <int> 5, 4, 4, 4, NA, 6, 3, NA, 6, 5, 5, 5, NA, NA, 5, 5, 4…
## $ valence_61 <int> 1, 4, 3, 4, NA, 3, 1, NA, 3, 1, 5, 1, NA, NA, 1, 2, 2…
## $ valence_62 <int> 5, 4, 6, 9, NA, 8, 7, NA, 6, 5, 4, 7, NA, NA, 6, 5, 6…
## $ valence_63 <int> 5, 5, 4, 1, NA, 5, 3, NA, 5, 3, 3, 4, NA, NA, 5, 5, 5…
## $ valence_64 <int> 1, 1, 3, 1, NA, 1, 3, NA, 1, 1, 3, 1, NA, NA, 1, 1, 2…
## $ valence_65 <int> 8, 5, 6, 7, NA, 2, 7, NA, 5, 1, 6, 5, NA, NA, 4, 5, 8…
## $ valence_66 <int> 2, 2, 2, 2, NA, 1, 4, NA, 2, 4, 6, 3, NA, NA, 2, 1, 2…
## $ valence_67 <int> 5, 5, 3, 1, NA, 1, 7, NA, 5, 4, 6, 5, NA, NA, 5, 3, 7…
## $ valence_68 <int> 5, 5, 3, 1, NA, 2, 1, NA, 5, 4, 7, 5, NA, NA, 5, 5, 6…
## $ valence_69 <int> 3, 4, 3, 1, NA, 2, 4, NA, 2, 3, 3, 3, NA, NA, 1, 3, 2…
## $ valence_70 <int> 1, 6, 3, 1, NA, 5, 4, NA, 5, 1, 7, 5, NA, NA, 2, 1, 3…
## $ valence_71 <int> 5, 5, 6, 1, NA, 7, 4, NA, 5, 5, 6, 5, NA, NA, 5, 7, 5…
## $ valence_72 <int> 5, 5, 5, 1, NA, 1, 3, NA, 5, 4, 5, 4, NA, NA, 5, 5, 5…
## $ valence_73 <int> 4, 4, 5, 4, NA, 4, 5, NA, 4, 1, 6, 4, NA, NA, 5, 3, 6…
## $ valence_74 <int> 4, 4, 5, 1, NA, 4, 5, NA, 4, 4, 5, 3, NA, NA, 4, 3, 5…
## $ valence_75 <int> 5, 5, 3, 5, NA, 5, 5, NA, 5, 5, 5, 6, NA, NA, 5, 5, 6…
## $ valence_76 <int> 6, 5, 5, 5, NA, 6, 5, NA, 6, 6, 7, 7, NA, NA, 5, 5, 7…
## $ valence_77 <int> 7, 5, 5, 6, NA, 6, 6, NA, 6, 4, 7, 6, NA, NA, 5, 5, 7…
## $ valence_78 <int> 7, 6, 5, 7, NA, 2, 7, NA, 7, 6, 7, 5, NA, NA, 5, 5, 7…
## $ valence_79 <int> 6, 4, 6, 1, NA, 2, 5, NA, 7, 1, 7, 7, NA, NA, 3, 5, 5…
## $ valence_80 <int> 9, 5, 5, 5, NA, 5, 5, NA, 6, 5, 4, 6, NA, NA, 5, 7, 7…
## $ valence_81 <int> 1, 1, 1, 1, NA, 1, 7, NA, 1, 1, 1, 3, NA, NA, 1, 1, 1…
## $ valence_82 <int> 5, 5, 7, 6, NA, 3, 7, NA, 6, 5, 7, 5, NA, NA, 6, 4, 7…
## $ valence_83 <int> 9, 5, 3, 1, NA, 2, 2, NA, 6, 1, 6, 5, NA, NA, 5, 5, 7…
## $ valence_84 <int> 5, 5, 5, 4, NA, 5, 5, NA, 6, 1, 4, 5, NA, NA, 5, 8, 4…
## $ valence_85 <int> 3, 4, 5, 5, NA, 3, 7, NA, 5, 5, 6, 5, NA, NA, 5, 7, 5…
## $ valence_86 <int> 5, 4, 6, 5, NA, 7, 5, NA, 7, 4, 4, 5, NA, NA, 5, 6, 5…
## $ valence_87 <int> 5, 1, 5, 4, NA, 2, 2, NA, 7, 1, 4, 5, NA, NA, 1, 6, 3…
## $ valence_88 <int> 5, 2, 5, 4, NA, 2, 3, NA, 7, 1, 4, 7, NA, NA, 1, 7, 5…
## $ valence_89 <int> 5, 1, 5, 1, NA, 1, 3, NA, 9, 2, 5, 7, NA, NA, 2, 5, 6…
## $ valence_90 <int> 4, 5, 5, 5, NA, 5, 8, NA, 5, 4, 5, 5, NA, NA, 5, 5, 5…
## $ valence_91 <int> 7, 5, 5, 7, NA, 7, 6, NA, 9, 2, 5, 6, NA, NA, 5, 5, 8…
## $ valence_92 <int> 5, 5, 6, 2, NA, 8, 5, NA, 7, 4, 4, 7, NA, NA, 5, 6, 4…
## $ valence_93 <int> 9, 5, 7, 7, NA, 5, 5, NA, 9, 5, 7, 8, NA, NA, 5, 4, 3…
## $ valence_94 <int> 3, 2, 5, 3, NA, 1, 3, NA, 5, 3, 5, 5, NA, NA, 2, 7, 4…
## $ valence_95 <int> 8, 4, 2, 4, NA, 3, 5, NA, 4, 3, 3, 5, NA, NA, 5, 2, 4…
## $ valence_96 <int> 3, 4, 4, 1, NA, 1, 2, NA, 8, 1, 5, 5, NA, NA, 1, 5, 5…
## $ valence_97 <int> 7, 6, 2, 2, NA, 3, 6, NA, 2, 4, 4, 5, NA, NA, 5, 5, 8…
## $ valence_98 <int> 5, 4, 7, 1, NA, 1, 5, NA, 8, 1, 5, 6, NA, NA, 4, 5, 5…
## $ valence_99 <int> 6, 5, 5, 1, NA, 6, 5, NA, 3, 4, 4, 4, NA, NA, 3, 3, 7…
## $ valence_100 <int> 8, 7, 7, 8, NA, 5, 8, NA, 7, 7, 8, 5, NA, NA, 7, 8, 8…
## $ arousal_1 <int> NA, 4, NA, 1, 7, NA, NA, 9, 7, 8, NA, NA, 7, 1, 6, 2,…
## $ arousal_2 <int> NA, 5, NA, 5, 6, NA, NA, 6, 5, 2, NA, NA, 3, 1, 1, 4,…
## $ arousal_3 <int> NA, 5, NA, 9, 2, NA, NA, 5, 3, 7, NA, NA, 3, 6, 5, 5,…
## $ arousal_4 <int> NA, 5, NA, 5, 4, NA, NA, 5, 4, 1, NA, NA, 2, 1, 1, 6,…
## $ arousal_5 <int> NA, 2, NA, 1, 1, NA, NA, 8, 6, 3, NA, NA, 5, 1, 1, 1,…
## $ arousal_6 <int> NA, 5, NA, 5, 6, NA, NA, 1, 1, 5, NA, NA, 2, 5, 1, 1,…
## $ arousal_7 <int> NA, 5, NA, 2, 2, NA, NA, 1, 3, 1, NA, NA, 3, 5, 1, 5,…
## $ arousal_8 <int> NA, 5, NA, 5, 7, NA, NA, 1, 2, 6, NA, NA, 2, 5, 1, 1,…
## $ arousal_9 <int> NA, 5, NA, 5, 5, NA, NA, 1, 2, 5, NA, NA, 5, 5, 1, 5,…
## $ arousal_10 <int> NA, 5, NA, 5, 6, NA, NA, 4, 5, 5, NA, NA, 5, 6, 1, 6,…
## $ arousal_11 <int> NA, 6, NA, 5, 5, NA, NA, 1, 4, 5, NA, NA, 4, 6, 2, 1,…
## $ arousal_12 <int> NA, 6, NA, 5, 5, NA, NA, 1, 4, 2, NA, NA, 3, 5, 1, 5,…
## $ arousal_13 <int> NA, 3, NA, 1, 7, NA, NA, 6, 7, 8, NA, NA, 3, 9, 1, 1,…
## $ arousal_14 <int> NA, 2, NA, 1, 9, NA, NA, 6, 8, 7, NA, NA, 5, 9, 1, 1,…
## $ arousal_15 <int> NA, 1, NA, 1, 7, NA, NA, 6, 6, 8, NA, NA, 5, 7, 1, 5,…
## $ arousal_16 <int> NA, 5, NA, 1, 4, NA, NA, 6, 8, 6, NA, NA, 4, 6, 1, 5,…
## $ arousal_17 <int> NA, 6, NA, 1, 3, NA, NA, 9, 6, 9, NA, NA, 7, 1, 9, 8,…
## $ arousal_18 <int> NA, 2, NA, 1, 8, NA, NA, 6, 8, 2, NA, NA, 4, 8, 1, 1,…
## $ arousal_19 <int> NA, 6, NA, 7, 6, NA, NA, 5, 5, 9, NA, NA, 8, 4, 7, 6,…
## $ arousal_20 <int> NA, 5, NA, 4, 2, NA, NA, 1, 1, 1, NA, NA, 1, 5, 1, 4,…
## $ arousal_21 <int> NA, 5, NA, 6, 6, NA, NA, 5, 5, 9, NA, NA, 3, 6, 1, 5,…
## $ arousal_22 <int> NA, 5, NA, 2, 1, NA, NA, 5, 4, 2, NA, NA, 8, 1, 1, 2,…
## $ arousal_23 <int> NA, 4, NA, 5, 3, NA, NA, 7, 4, 6, NA, NA, 5, 1, 1, 1,…
## $ arousal_24 <int> NA, 2, NA, 1, 5, NA, NA, 7, 3, 3, NA, NA, 6, 1, 1, 1,…
## $ arousal_25 <int> NA, 4, NA, 1, 5, NA, NA, 8, 7, 9, NA, NA, 6, 1, 1, 7,…
## $ arousal_26 <int> NA, 4, NA, 1, 5, NA, NA, 2, 6, 6, NA, NA, 3, 1, 1, 1,…
## $ arousal_27 <int> NA, 5, NA, 6, 1, NA, NA, 1, 4, 7, NA, NA, 5, 1, 1, 1,…
## $ arousal_28 <int> NA, 5, NA, 9, 8, NA, NA, 6, 4, 9, NA, NA, 5, 5, 8, 6,…
## $ arousal_29 <int> NA, 6, NA, 1, 5, NA, NA, 9, 6, 9, NA, NA, 6, 1, 6, 7,…
## $ arousal_30 <int> NA, 5, NA, 1, 6, NA, NA, 1, 4, 5, NA, NA, 3, 5, 1, 1,…
## $ arousal_31 <int> NA, 5, NA, 1, 5, NA, NA, 1, 2, 1, NA, NA, 1, 5, 1, 5,…
## $ arousal_32 <int> NA, 5, NA, 7, 5, NA, NA, 1, 3, 1, NA, NA, 2, 1, 1, 1,…
## $ arousal_33 <int> NA, 6, NA, 4, 6, NA, NA, 7, 6, 9, NA, NA, 6, 1, 1, 6,…
## $ arousal_34 <int> NA, 5, NA, 1, 2, NA, NA, 7, 4, 5, NA, NA, 5, 1, 1, 3,…
## $ arousal_35 <int> NA, 5, NA, 1, 5, NA, NA, 2, 4, 3, NA, NA, 3, 1, 1, 7,…
## $ arousal_36 <int> NA, 5, NA, 1, 1, NA, NA, 5, 6, 5, NA, NA, 5, 6, 1, 1,…
## $ arousal_37 <int> NA, 5, NA, 1, 6, NA, NA, 7, 5, 4, NA, NA, 4, 1, 7, 7,…
## $ arousal_38 <int> NA, 6, NA, 1, 8, NA, NA, 9, 6, 9, NA, NA, 8, 1, 9, 9,…
## $ arousal_39 <int> NA, 7, NA, 1, 5, NA, NA, 8, 7, 7, NA, NA, 6, 1, 1, 8,…
## $ arousal_40 <int> NA, 3, NA, 6, 2, NA, NA, 2, 3, 1, NA, NA, 3, 1, 1, 1,…
## $ arousal_41 <int> NA, 5, NA, 1, 5, NA, NA, 6, 8, 7, NA, NA, 3, 9, 1, 5,…
## $ arousal_42 <int> NA, 6, NA, 5, 4, NA, NA, 6, 9, 7, NA, NA, 6, 6, 1, 7,…
## $ arousal_43 <int> NA, 5, NA, 1, 7, NA, NA, 6, 8, 4, NA, NA, 3, 9, 1, 1,…
## $ arousal_44 <int> NA, 5, NA, 5, 3, NA, NA, 1, 1, 1, NA, NA, 5, 5, 1, 1,…
## $ arousal_45 <int> NA, 5, NA, 1, 4, NA, NA, 9, 4, 6, NA, NA, 7, 1, 6, 6,…
## $ arousal_46 <int> NA, 4, NA, 1, 6, NA, NA, 8, 4, 5, NA, NA, 8, 1, 1, 6,…
## $ arousal_47 <int> NA, 6, NA, 6, 6, NA, NA, 7, 8, 7, NA, NA, 6, 1, 1, 7,…
## $ arousal_48 <int> NA, 6, NA, 5, 7, NA, NA, 5, 4, 4, NA, NA, 3, 5, 1, 8,…
## $ arousal_49 <int> NA, 5, NA, 2, 2, NA, NA, 2, 5, 8, NA, NA, 3, 5, 1, 1,…
## $ arousal_50 <int> NA, 6, NA, 1, 8, NA, NA, 9, 7, 9, NA, NA, 6, 1, 6, 9,…
## $ arousal_51 <int> 7, 5, 4, 1, NA, 8, 6, NA, 7, 6, 7, 6, NA, NA, 1, 4, 5…
## $ arousal_52 <int> 9, 9, 7, 9, NA, 6, 7, NA, 7, 9, 9, 8, NA, NA, 1, 6, 7…
## $ arousal_53 <int> 9, 6, 8, 1, NA, 9, 7, NA, 9, 9, 8, 8, NA, NA, 6, 6, 2…
## $ arousal_54 <int> 8, 5, 4, 4, NA, 5, 5, NA, 8, 1, 7, 6, NA, NA, 2, 6, 5…
## $ arousal_55 <int> 2, 5, 3, 1, NA, 5, 2, NA, 3, 3, 3, 3, NA, NA, 1, 1, 5…
## $ arousal_56 <int> 6, 5, 2, 5, NA, 5, 3, NA, 1, 2, 6, 3, NA, NA, 1, 1, 5…
## $ arousal_57 <int> 2, 5, 4, 1, NA, 5, 2, NA, 3, 4, 4, 2, NA, NA, 4, 3, 6…
## $ arousal_58 <int> 4, 6, 4, 6, NA, 5, 6, NA, 3, 4, 6, 6, NA, NA, 1, 2, 5…
## $ arousal_59 <int> 7, 4, 6, 1, NA, 7, 3, NA, 4, 6, 3, 2, NA, NA, 1, 7, 2…
## $ arousal_60 <int> 1, 5, 5, 1, NA, 5, 5, NA, 4, 1, 6, 6, NA, NA, 1, 3, 3…
## $ arousal_61 <int> 9, 6, 6, 1, NA, 8, 7, NA, 6, 9, 6, 8, NA, NA, 7, 9, 5…
## $ arousal_62 <int> 5, 4, 6, 1, NA, 5, 4, NA, 6, 6, 7, 6, NA, NA, 4, 1, 5…
## $ arousal_63 <int> 5, 5, 1, 5, NA, 5, 1, NA, 1, 1, 1, 1, NA, NA, 1, 1, 5…
## $ arousal_64 <int> 9, 7, 8, NA, NA, 7, 7, NA, 9, 9, 9, 8, NA, NA, 9, 6, …
## $ arousal_65 <int> 6, 5, 6, 9, NA, 5, 6, NA, 7, 9, 5, 6, NA, NA, 1, 1, 6…
## $ arousal_66 <int> 8, 5, 5, 5, NA, 2, 7, NA, 8, 7, 6, 6, NA, NA, 1, 8, 4…
## $ arousal_67 <int> 4, 5, 3, 5, NA, 6, 2, NA, 1, 4, 5, 3, NA, NA, 1, 6, 4…
## $ arousal_68 <int> 5, 5, 5, 1, NA, 4, 2, NA, 3, 5, 3, 2, NA, NA, 1, 1, 5…
## $ arousal_69 <int> 8, 6, 6, 1, NA, 6, 4, NA, 7, 8, 7, 7, NA, NA, 1, 7, 5…
## $ arousal_70 <int> 9, 6, 6, 1, NA, 5, 3, NA, 6, 8, 6, 5, NA, NA, 4, 8, 6…
## $ arousal_71 <int> 7, 5, 2, 5, NA, 7, 4, NA, 2, 5, 6, 2, NA, NA, 1, 6, 5…
## $ arousal_72 <int> 2, 5, 2, 5, NA, 2, 3, NA, 1, 3, 1, 1, NA, NA, 1, 1, 3…
## $ arousal_73 <int> 7, 6, 7, 1, NA, 7, 7, NA, 7, 6, 6, 6, NA, NA, 1, 6, 5…
## $ arousal_74 <int> 6, 5, 2, 5, NA, 6, 5, NA, 4, 6, 5, 7, NA, NA, 1, 4, 5…
## $ arousal_75 <int> 1, 5, 4, 1, NA, 5, 5, NA, 2, 5, 3, 2, NA, NA, 1, 1, 5…
## $ arousal_76 <int> 5, 4, 4, 5, NA, 5, 5, NA, 4, 6, 6, 3, NA, NA, 1, 1, 5…
## $ arousal_77 <int> 5, 5, 4, 5, NA, 6, 5, NA, 5, 4, 5, 6, NA, NA, 1, 1, 6…
## $ arousal_78 <int> 5, 5, 7, 1, NA, 3, 3, NA, 6, 6, 3, 6, NA, NA, 1, 1, 6…
## $ arousal_79 <int> 5, 5, 6, 1, NA, 6, 8, NA, 8, 6, 6, 7, NA, NA, 1, 1, 6…
## $ arousal_80 <int> 7, 5, 4, 1, NA, 5, 5, NA, 5, 5, 3, 5, NA, NA, 1, 6, 5…
## $ arousal_81 <int> 8, 6, 8, 1, NA, 1, 7, NA, 9, 8, 9, 6, NA, NA, 6, 8, 1…
## $ arousal_82 <int> 5, 5, 5, 5, NA, 8, 6, NA, 5, 5, 6, 6, NA, NA, 5, 3, 5…
## $ arousal_83 <int> 3, 5, 5, 1, NA, 3, 2, NA, 4, 7, 2, 3, NA, NA, 1, 1, 5…
## $ arousal_84 <int> 5, 5, 4, 1, NA, 5, 3, NA, 4, 7, 5, 4, NA, NA, 1, 7, 5…
## $ arousal_85 <int> 6, 5, 4, 5, NA, 4, 7, NA, 6, 5, 6, 6, NA, NA, 1, 7, 5…
## $ arousal_86 <int> 5, 5, 8, 5, NA, 5, 3, NA, 5, 8, 6, 6, NA, NA, 1, 4, 5…
## $ arousal_87 <int> 5, 6, 5, 2, NA, 5, 7, NA, 8, 9, 5, 8, NA, NA, 1, 5, 2…
## $ arousal_88 <int> 5, 5, 4, 1, NA, 2, 7, NA, 8, 8, 6, 8, NA, NA, 1, 6, 5…
## $ arousal_89 <int> 5, 5, 6, 1, NA, 6, 6, NA, 9, 8, 5, 8, NA, NA, 1, 1, 6…
## $ arousal_90 <int> 5, 5, 3, 5, NA, 5, 4, NA, 2, 3, 2, 3, NA, NA, 1, 1, 5…
## $ arousal_91 <int> 5, 4, 4, 1, NA, 5, 5, NA, 7, 6, 5, 5, NA, NA, 1, 1, 6…
## $ arousal_92 <int> 5, 6, 6, 1, NA, 7, 6, NA, 6, 5, 7, 5, NA, NA, 1, 7, 5…
## $ arousal_93 <int> 6, 5, 5, 5, NA, 2, 3, NA, 6, 5, 8, 8, NA, NA, 1, 3, 1…
## $ arousal_94 <int> 5, 6, 7, 1, NA, 1, 6, NA, 9, 7, 7, 6, NA, NA, 1, 7, 3…
## $ arousal_95 <int> 6, 6, 9, 1, NA, 5, 7, NA, 8, 7, 7, 7, NA, NA, 1, 8, 3…
## $ arousal_96 <int> 5, 5, 5, 1, NA, 5, 6, NA, 6, 8, 6, 6, NA, NA, 1, 1, 4…
## $ arousal_97 <int> 5, 5, 7, 1, NA, 5, 6, NA, 8, 8, 7, 7, NA, NA, 1, 1, 5…
## $ arousal_98 <int> 5, 5, 8, 3, NA, 1, 6, NA, 7, 8, 5, 8, NA, NA, 1, 1, 6…
## $ arousal_99 <int> 5, 5, 7, 1, NA, 6, 6, NA, 7, 7, 6, 7, NA, NA, 1, 5, 5…
## $ arousal_100 <int> 2, 4, 6, 9, NA, 5, 3, NA, 4, 6, 3, 3, NA, NA, 1, 8, 6…
## $ residence_all <dbl+lbl> 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2…
## $ region_all <dbl+lbl> 3, 3, 2, 3, 3, 2, 3, 1, 3, 3, 3, 4, 4, 4, 1, 3, 2…
kbl(describe(image_ratings_human[,c(20:219)])) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
full_width = F,
fixed_thead = F)| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| valence_1 | 1 | 541 | 3.349353 | 1.5073722 | 3.0 | 3.270208 | 1.4826 | 1 | 9 | 8 | 0.6001275 | 0.6034164 | 0.0648070 |
| valence_2 | 2 | 541 | 3.628466 | 1.1613543 | 4.0 | 3.653580 | 1.4826 | 1 | 8 | 7 | 0.0100716 | 0.4366718 | 0.0499305 |
| valence_3 | 3 | 539 | 7.220779 | 1.4238589 | 7.0 | 7.355658 | 1.4826 | 1 | 9 | 8 | -1.1782227 | 2.6186488 | 0.0613299 |
| valence_4 | 4 | 540 | 5.709259 | 1.7041659 | 6.0 | 5.773148 | 1.4826 | 1 | 9 | 8 | -0.3129542 | 0.0186661 | 0.0733356 |
| valence_5 | 5 | 540 | 4.674074 | 1.6518034 | 5.0 | 4.666667 | 1.4826 | 1 | 9 | 8 | 0.1021700 | 0.0499273 | 0.0710823 |
| valence_6 | 6 | 541 | 4.731978 | 0.8945688 | 5.0 | 4.817552 | 0.0000 | 1 | 9 | 8 | -0.6432281 | 4.9746022 | 0.0384605 |
| valence_7 | 7 | 541 | 4.741220 | 1.1236274 | 5.0 | 4.815243 | 0.0000 | 1 | 9 | 8 | -0.5281457 | 3.1761768 | 0.0483085 |
| valence_8 | 8 | 541 | 4.563771 | 0.9879364 | 5.0 | 4.683603 | 0.0000 | 1 | 9 | 8 | -0.9649011 | 3.0662846 | 0.0424747 |
| valence_9 | 9 | 541 | 4.680222 | 0.9125446 | 5.0 | 4.799076 | 0.0000 | 1 | 8 | 7 | -1.0652033 | 3.6193454 | 0.0392334 |
| valence_10 | 10 | 541 | 5.970425 | 1.4359968 | 6.0 | 5.986143 | 1.4826 | 1 | 9 | 8 | -0.3303338 | 1.1790044 | 0.0617383 |
| valence_11 | 11 | 541 | 6.051756 | 1.4224072 | 6.0 | 6.057737 | 1.4826 | 1 | 9 | 8 | -0.1950718 | 0.6843469 | 0.0611541 |
| valence_12 | 12 | 541 | 5.711645 | 1.4472302 | 6.0 | 5.706697 | 1.4826 | 1 | 9 | 8 | -0.1930798 | 1.0563398 | 0.0622213 |
| valence_13 | 13 | 540 | 4.831481 | 2.0192326 | 5.0 | 4.821759 | 1.4826 | 1 | 9 | 8 | 0.0208343 | -0.4423392 | 0.0868939 |
| valence_14 | 14 | 541 | 4.624769 | 2.1597445 | 4.0 | 4.545035 | 2.9652 | 1 | 9 | 8 | 0.2592631 | -0.6809424 | 0.0928547 |
| valence_15 | 15 | 541 | 5.367837 | 1.5961762 | 5.0 | 5.385681 | 1.4826 | 1 | 9 | 8 | -0.1973276 | 0.4925717 | 0.0686250 |
| valence_16 | 16 | 541 | 5.384473 | 1.5456208 | 5.0 | 5.406466 | 1.4826 | 1 | 9 | 8 | -0.0914562 | 0.3706683 | 0.0664514 |
| valence_17 | 17 | 541 | 2.035120 | 1.2385271 | 2.0 | 1.836028 | 1.4826 | 1 | 9 | 8 | 1.9008363 | 5.9431645 | 0.0532484 |
| valence_18 | 18 | 541 | 5.454714 | 1.8639566 | 5.0 | 5.464203 | 1.4826 | 1 | 9 | 8 | -0.0274533 | -0.4120613 | 0.0801378 |
| valence_19 | 19 | 541 | 6.377079 | 1.5135657 | 6.0 | 6.459584 | 1.4826 | 1 | 9 | 8 | -0.7294959 | 1.2968182 | 0.0650733 |
| valence_20 | 20 | 541 | 4.319778 | 1.1798292 | 5.0 | 4.468822 | 0.0000 | 1 | 9 | 8 | -0.8123874 | 1.3418300 | 0.0507248 |
| valence_21 | 21 | 541 | 6.741220 | 1.4101292 | 7.0 | 6.780601 | 1.4826 | 1 | 9 | 8 | -0.6598813 | 1.2185605 | 0.0606262 |
| valence_22 | 22 | 540 | 5.066667 | 1.5371709 | 5.0 | 5.134259 | 1.4826 | 1 | 9 | 8 | -0.3869126 | 0.7905558 | 0.0661493 |
| valence_23 | 23 | 540 | 3.411111 | 1.6585827 | 3.0 | 3.358796 | 1.4826 | 1 | 9 | 8 | 0.2604819 | -0.4756324 | 0.0713740 |
| valence_24 | 24 | 541 | 3.508318 | 1.6606628 | 3.0 | 3.411085 | 1.4826 | 1 | 9 | 8 | 0.5702814 | 0.3158323 | 0.0713975 |
| valence_25 | 25 | 541 | 3.249538 | 1.6177902 | 3.0 | 3.166282 | 1.4826 | 1 | 8 | 7 | 0.4132266 | -0.4332239 | 0.0695542 |
| valence_26 | 26 | 540 | 4.811111 | 1.3962136 | 5.0 | 4.861111 | 1.4826 | 1 | 9 | 8 | -0.3068134 | 1.4323408 | 0.0600835 |
| valence_27 | 27 | 540 | 5.655556 | 1.4745417 | 6.0 | 5.659722 | 1.4826 | 1 | 9 | 8 | -0.2408259 | 1.0095537 | 0.0634542 |
| valence_28 | 28 | 540 | 7.377778 | 1.5282943 | 8.0 | 7.574074 | 1.4826 | 1 | 9 | 8 | -1.1350468 | 1.7092675 | 0.0657673 |
| valence_29 | 29 | 540 | 2.118518 | 1.2590414 | 2.0 | 1.935185 | 1.4826 | 1 | 9 | 8 | 1.2185776 | 1.7661504 | 0.0541805 |
| valence_30 | 30 | 540 | 5.411111 | 1.3489041 | 5.0 | 5.402778 | 1.4826 | 1 | 9 | 8 | -0.0989097 | 2.0350313 | 0.0580476 |
| valence_31 | 31 | 540 | 4.503704 | 1.1581706 | 5.0 | 4.604167 | 0.0000 | 1 | 9 | 8 | -0.7242003 | 2.2374773 | 0.0498397 |
| valence_32 | 32 | 540 | 5.001852 | 0.8582224 | 5.0 | 5.020833 | 0.0000 | 1 | 9 | 8 | -0.8824084 | 9.7503863 | 0.0369320 |
| valence_33 | 33 | 541 | 3.924214 | 1.7637779 | 4.0 | 3.898383 | 1.4826 | 1 | 9 | 8 | 0.2099316 | -0.3684396 | 0.0758307 |
| valence_34 | 34 | 540 | 4.177778 | 1.3599993 | 4.0 | 4.231482 | 1.4826 | 1 | 9 | 8 | -0.2302302 | 0.7820723 | 0.0585251 |
| valence_35 | 35 | 540 | 6.055556 | 1.5769286 | 6.0 | 6.076389 | 1.4826 | 1 | 9 | 8 | -0.3378847 | 0.8812250 | 0.0678602 |
| valence_36 | 36 | 541 | 5.207024 | 1.4329543 | 5.0 | 5.228637 | 1.4826 | 1 | 9 | 8 | -0.2371204 | 1.6428786 | 0.0616075 |
| valence_37 | 37 | 540 | 6.946296 | 1.6032450 | 7.0 | 7.069444 | 1.4826 | 1 | 9 | 8 | -0.8240173 | 0.9849065 | 0.0689927 |
| valence_38 | 38 | 540 | 1.524074 | 1.0397365 | 1.0 | 1.280093 | 0.0000 | 1 | 9 | 8 | 2.8818128 | 11.8304994 | 0.0447431 |
| valence_39 | 39 | 541 | 3.284658 | 1.8728878 | 3.0 | 3.117783 | 1.4826 | 1 | 9 | 8 | 0.6246140 | -0.0077511 | 0.0805217 |
| valence_40 | 40 | 540 | 5.637037 | 1.2942425 | 5.0 | 5.648148 | 1.4826 | 1 | 9 | 8 | -0.4228844 | 2.3619679 | 0.0556953 |
| valence_41 | 41 | 541 | 4.988909 | 2.1195446 | 5.0 | 4.981524 | 1.4826 | 1 | 9 | 8 | 0.0214924 | -0.5970282 | 0.0911263 |
| valence_42 | 42 | 540 | 4.324074 | 1.9235322 | 4.0 | 4.280093 | 1.4826 | 1 | 9 | 8 | 0.1966414 | -0.3848232 | 0.0827756 |
| valence_43 | 43 | 540 | 4.661111 | 2.1984070 | 5.0 | 4.615741 | 2.9652 | 1 | 9 | 8 | 0.1604229 | -0.7194946 | 0.0946044 |
| valence_44 | 44 | 541 | 5.025878 | 0.9305889 | 5.0 | 5.011547 | 0.0000 | 1 | 9 | 8 | 0.0176291 | 7.4636251 | 0.0400091 |
| valence_45 | 45 | 539 | 3.775510 | 1.9746708 | 4.0 | 3.651270 | 1.4826 | 1 | 9 | 8 | 0.4298373 | -0.4220516 | 0.0850551 |
| valence_46 | 46 | 540 | 4.337037 | 1.7794223 | 4.0 | 4.300926 | 1.4826 | 1 | 9 | 8 | 0.1366322 | -0.2490410 | 0.0765741 |
| valence_47 | 47 | 540 | 3.935185 | 2.0543323 | 4.0 | 3.840278 | 2.9652 | 1 | 9 | 8 | 0.2805496 | -0.6688910 | 0.0884044 |
| valence_48 | 48 | 541 | 4.850277 | 1.5437283 | 5.0 | 4.840647 | 1.4826 | 1 | 9 | 8 | 0.0004210 | 0.6043703 | 0.0663701 |
| valence_49 | 49 | 540 | 4.659259 | 1.6577830 | 5.0 | 4.699074 | 1.4826 | 1 | 9 | 8 | -0.1505609 | 0.2923431 | 0.0713396 |
| valence_50 | 50 | 540 | 2.001852 | 1.2929078 | 2.0 | 1.773148 | 1.4826 | 1 | 9 | 8 | 1.6365740 | 3.5284329 | 0.0556379 |
| valence_51 | 51 | 535 | 4.837383 | 1.9308516 | 5.0 | 4.829837 | 1.4826 | 1 | 9 | 8 | 0.0261056 | -0.4419437 | 0.0834780 |
| valence_52 | 52 | 536 | 8.029851 | 1.2601160 | 8.0 | 8.260465 | 1.4826 | 1 | 9 | 8 | -1.7623263 | 4.0879337 | 0.0544287 |
| valence_53 | 53 | 536 | 2.130597 | 1.2506882 | 2.0 | 1.953488 | 1.4826 | 1 | 9 | 8 | 1.3607913 | 2.7849185 | 0.0540215 |
| valence_54 | 54 | 536 | 3.044776 | 1.3429813 | 3.0 | 2.983721 | 1.4826 | 1 | 9 | 8 | 0.5748561 | 0.6778795 | 0.0580080 |
| valence_55 | 55 | 536 | 4.772388 | 1.2214832 | 5.0 | 4.811628 | 0.0000 | 1 | 9 | 8 | -0.3159328 | 2.0045545 | 0.0527600 |
| valence_56 | 56 | 535 | 5.074766 | 1.2697941 | 5.0 | 5.097902 | 0.0000 | 1 | 9 | 8 | -0.3098065 | 2.0131102 | 0.0548980 |
| valence_57 | 57 | 536 | 5.481343 | 1.4887826 | 5.0 | 5.453488 | 1.4826 | 1 | 9 | 8 | -0.0724402 | 0.7411982 | 0.0643056 |
| valence_58 | 58 | 536 | 5.722015 | 1.5184721 | 6.0 | 5.753488 | 1.4826 | 1 | 9 | 8 | -0.3659351 | 0.7212373 | 0.0655880 |
| valence_59 | 59 | 535 | 6.485981 | 1.6802693 | 7.0 | 6.578089 | 1.4826 | 1 | 9 | 8 | -0.5703198 | 0.3545438 | 0.0726444 |
| valence_60 | 60 | 536 | 4.880597 | 1.5433658 | 5.0 | 4.960465 | 1.4826 | 1 | 9 | 8 | -0.5067813 | 0.6854062 | 0.0666632 |
| valence_61 | 61 | 536 | 2.192164 | 1.4346885 | 2.0 | 1.967442 | 1.4826 | 1 | 9 | 8 | 1.4166661 | 2.3074799 | 0.0619691 |
| valence_62 | 62 | 536 | 5.569030 | 1.4217574 | 5.0 | 5.534884 | 1.4826 | 1 | 9 | 8 | 0.0026730 | 0.4739881 | 0.0614106 |
| valence_63 | 63 | 536 | 4.746269 | 0.9042915 | 5.0 | 4.846512 | 0.0000 | 1 | 9 | 8 | -0.9037931 | 6.0652404 | 0.0390594 |
| valence_64 | 64 | 536 | 1.682836 | 1.1208159 | 1.0 | 1.451163 | 0.0000 | 1 | 9 | 8 | 2.5210170 | 8.7662274 | 0.0484119 |
| valence_65 | 65 | 536 | 5.671642 | 1.7327030 | 6.0 | 5.686046 | 1.4826 | 1 | 9 | 8 | -0.1172004 | 0.1737979 | 0.0748414 |
| valence_66 | 66 | 536 | 2.626866 | 1.5208047 | 2.0 | 2.446512 | 1.4826 | 1 | 9 | 8 | 0.9520743 | 0.6938394 | 0.0656888 |
| valence_67 | 67 | 536 | 4.192164 | 1.4281595 | 4.0 | 4.200000 | 1.4826 | 1 | 9 | 8 | 0.0718130 | 0.9511162 | 0.0616871 |
| valence_68 | 68 | 536 | 4.863806 | 1.2656924 | 5.0 | 4.916279 | 0.0000 | 1 | 9 | 8 | -0.3896465 | 2.0428691 | 0.0546696 |
| valence_69 | 69 | 534 | 2.953184 | 1.4193976 | 3.0 | 2.869159 | 1.4826 | 1 | 9 | 8 | 0.5775011 | 0.4777612 | 0.0614233 |
| valence_70 | 70 | 536 | 3.436567 | 1.8794726 | 3.0 | 3.288372 | 1.4826 | 1 | 9 | 8 | 0.5547969 | -0.2136271 | 0.0811809 |
| valence_71 | 71 | 536 | 5.257463 | 1.0647014 | 5.0 | 5.225581 | 0.0000 | 1 | 9 | 8 | -0.0333550 | 3.8304950 | 0.0459881 |
| valence_72 | 72 | 536 | 4.897388 | 0.8832357 | 5.0 | 4.962791 | 0.0000 | 1 | 9 | 8 | -0.6930263 | 9.9970766 | 0.0381500 |
| valence_73 | 73 | 536 | 3.882463 | 1.7302126 | 4.0 | 3.865116 | 1.4826 | 1 | 9 | 8 | 0.1421369 | -0.4706922 | 0.0747338 |
| valence_74 | 74 | 536 | 4.022388 | 1.4311170 | 4.0 | 4.079070 | 1.4826 | 1 | 9 | 8 | -0.2110689 | 0.6428369 | 0.0618148 |
| valence_75 | 75 | 536 | 5.593284 | 1.2994145 | 5.0 | 5.581395 | 1.4826 | 1 | 9 | 8 | -0.1001140 | 1.5371138 | 0.0561262 |
| valence_76 | 76 | 534 | 6.140449 | 1.4742736 | 6.0 | 6.135514 | 1.4826 | 1 | 9 | 8 | -0.1195706 | 0.5322742 | 0.0637981 |
| valence_77 | 77 | 536 | 6.266791 | 1.6569586 | 6.0 | 6.323256 | 1.4826 | 1 | 9 | 8 | -0.5088666 | 0.5475596 | 0.0715697 |
| valence_78 | 78 | 536 | 5.623134 | 1.5321313 | 5.0 | 5.616279 | 1.4826 | 1 | 9 | 8 | -0.1376852 | 0.6873413 | 0.0661780 |
| valence_79 | 79 | 536 | 5.591418 | 1.7590995 | 6.0 | 5.623256 | 1.4826 | 1 | 9 | 8 | -0.1381898 | -0.1099905 | 0.0759815 |
| valence_80 | 80 | 536 | 5.919776 | 1.3308351 | 6.0 | 5.897674 | 1.4826 | 1 | 9 | 8 | -0.1192622 | 1.0407881 | 0.0574833 |
| valence_81 | 81 | 535 | 1.484112 | 1.0071049 | 1.0 | 1.233100 | 0.0000 | 1 | 9 | 8 | 3.0736322 | 12.1014880 | 0.0435409 |
| valence_82 | 82 | 536 | 5.919776 | 1.6886373 | 6.0 | 5.965116 | 1.4826 | 1 | 9 | 8 | -0.3183236 | 0.4404850 | 0.0729380 |
| valence_83 | 83 | 536 | 4.945895 | 1.6896778 | 5.0 | 4.972093 | 1.4826 | 1 | 9 | 8 | -0.1055782 | 0.5273851 | 0.0729830 |
| valence_84 | 84 | 536 | 3.916045 | 1.6355982 | 4.0 | 3.913953 | 1.4826 | 1 | 9 | 8 | 0.1934920 | 0.4293696 | 0.0706471 |
| valence_85 | 85 | 536 | 5.208955 | 1.7723721 | 5.0 | 5.218605 | 1.4826 | 1 | 9 | 8 | -0.1120560 | -0.0357700 | 0.0765548 |
| valence_86 | 86 | 536 | 5.117537 | 1.2660891 | 5.0 | 5.132558 | 0.0000 | 1 | 9 | 8 | -0.3146368 | 2.1280314 | 0.0546867 |
| valence_87 | 87 | 536 | 4.218284 | 1.8733053 | 4.0 | 4.186046 | 1.4826 | 1 | 9 | 8 | 0.1628912 | -0.5094232 | 0.0809145 |
| valence_88 | 88 | 534 | 4.970038 | 1.8476295 | 5.0 | 5.007009 | 1.4826 | 1 | 9 | 8 | -0.1040446 | -0.3575186 | 0.0799547 |
| valence_89 | 89 | 533 | 5.442777 | 1.9201664 | 5.0 | 5.473068 | 1.4826 | 1 | 9 | 8 | -0.1011778 | -0.3310685 | 0.0831716 |
| valence_90 | 90 | 536 | 5.052239 | 1.1442970 | 5.0 | 5.041861 | 0.0000 | 1 | 9 | 8 | 0.0773705 | 4.9972467 | 0.0494261 |
| valence_91 | 91 | 536 | 6.309702 | 1.5079785 | 6.0 | 6.313954 | 1.4826 | 1 | 9 | 8 | -0.2721737 | 0.4400108 | 0.0651348 |
| valence_92 | 92 | 536 | 5.658582 | 1.4715498 | 6.0 | 5.676744 | 1.4826 | 1 | 9 | 8 | -0.3027209 | 1.0189623 | 0.0635613 |
| valence_93 | 93 | 536 | 5.861940 | 1.5801240 | 6.0 | 5.865116 | 1.4826 | 1 | 9 | 8 | -0.1812764 | 0.7054291 | 0.0682510 |
| valence_94 | 94 | 536 | 4.091418 | 2.0336058 | 4.0 | 4.009302 | 1.4826 | 1 | 9 | 8 | 0.3646561 | -0.5227787 | 0.0878384 |
| valence_95 | 95 | 536 | 3.583955 | 1.5673193 | 4.0 | 3.544186 | 1.4826 | 1 | 9 | 8 | 0.2693887 | -0.2423997 | 0.0676979 |
| valence_96 | 96 | 536 | 4.619403 | 1.7796136 | 5.0 | 4.637209 | 1.4826 | 1 | 9 | 8 | -0.0787327 | 0.0553815 | 0.0768676 |
| valence_97 | 97 | 536 | 4.292910 | 1.8620725 | 4.0 | 4.216279 | 1.4826 | 1 | 9 | 8 | 0.3298455 | -0.3574011 | 0.0804293 |
| valence_98 | 98 | 536 | 5.210821 | 1.7321464 | 5.0 | 5.202326 | 1.4826 | 1 | 9 | 8 | -0.0435228 | 0.2418465 | 0.0748173 |
| valence_99 | 99 | 536 | 4.611940 | 1.6367757 | 4.0 | 4.567442 | 1.4826 | 1 | 9 | 8 | 0.2799251 | 0.0111992 | 0.0706979 |
| valence_100 | 100 | 536 | 6.968284 | 1.4197939 | 7.0 | 7.034884 | 1.4826 | 1 | 9 | 8 | -0.5621498 | 0.4834454 | 0.0613258 |
| arousal_1 | 101 | 541 | 5.621072 | 2.3794334 | 6.0 | 5.810624 | 1.4826 | 1 | 9 | 8 | -0.6808376 | -0.6105638 | 0.1022998 |
| arousal_2 | 102 | 541 | 4.031423 | 2.0378210 | 4.0 | 3.963049 | 2.9652 | 1 | 9 | 8 | 0.1999880 | -0.8092622 | 0.0876128 |
| arousal_3 | 103 | 541 | 5.334566 | 2.1432252 | 6.0 | 5.418014 | 1.4826 | 1 | 9 | 8 | -0.3843177 | -0.6266262 | 0.0921444 |
| arousal_4 | 104 | 540 | 4.100000 | 2.0497522 | 4.0 | 4.057870 | 2.9652 | 1 | 9 | 8 | 0.0537751 | -0.8983071 | 0.0882073 |
| arousal_5 | 105 | 540 | 4.812963 | 2.2047803 | 5.0 | 4.870370 | 2.9652 | 1 | 9 | 8 | -0.2811748 | -0.9192271 | 0.0948786 |
| arousal_6 | 106 | 541 | 2.704251 | 1.8879149 | 2.0 | 2.542725 | 1.4826 | 1 | 9 | 8 | 0.5806180 | -1.1244280 | 0.0811678 |
| arousal_7 | 107 | 540 | 3.124074 | 2.0197021 | 3.0 | 2.949074 | 2.9652 | 1 | 9 | 8 | 0.4579905 | -0.9651087 | 0.0869141 |
| arousal_8 | 108 | 541 | 2.907579 | 1.9414480 | 2.0 | 2.745958 | 1.4826 | 1 | 9 | 8 | 0.5089204 | -1.0618684 | 0.0834694 |
| arousal_9 | 109 | 541 | 2.730129 | 1.8917946 | 2.0 | 2.577367 | 1.4826 | 1 | 9 | 8 | 0.5687614 | -1.0948468 | 0.0813346 |
| arousal_10 | 110 | 540 | 4.951852 | 1.9919821 | 5.0 | 4.976852 | 1.4826 | 1 | 9 | 8 | -0.1797011 | -0.4841428 | 0.0857213 |
| arousal_11 | 111 | 541 | 4.253235 | 2.1258144 | 5.0 | 4.244804 | 2.9652 | 1 | 9 | 8 | -0.0399629 | -1.0250309 | 0.0913959 |
| arousal_12 | 112 | 540 | 3.970370 | 2.0789277 | 4.0 | 3.914352 | 2.9652 | 1 | 9 | 8 | 0.0641157 | -0.9630114 | 0.0894628 |
| arousal_13 | 113 | 540 | 5.955556 | 2.2885264 | 6.0 | 6.173611 | 1.4826 | 1 | 9 | 8 | -0.7613257 | -0.2167046 | 0.0984825 |
| arousal_14 | 114 | 541 | 5.894640 | 2.3384842 | 6.0 | 6.094688 | 2.9652 | 1 | 9 | 8 | -0.6745831 | -0.4245017 | 0.1005393 |
| arousal_15 | 115 | 540 | 5.338889 | 2.0353934 | 6.0 | 5.442130 | 1.4826 | 1 | 9 | 8 | -0.4814427 | -0.3456202 | 0.0875894 |
| arousal_16 | 116 | 541 | 5.044362 | 2.1059595 | 5.0 | 5.096998 | 1.4826 | 1 | 9 | 8 | -0.3112584 | -0.5263692 | 0.0905423 |
| arousal_17 | 117 | 539 | 6.335807 | 2.5825476 | 7.0 | 6.662818 | 1.4826 | 1 | 9 | 8 | -0.9840896 | -0.2408397 | 0.1112382 |
| arousal_18 | 118 | 541 | 5.896488 | 2.1705961 | 6.0 | 6.064665 | 1.4826 | 1 | 9 | 8 | -0.6371854 | -0.2488783 | 0.0933212 |
| arousal_19 | 119 | 540 | 4.827778 | 2.1123294 | 5.0 | 4.842593 | 2.9652 | 1 | 9 | 8 | -0.1520111 | -0.8361072 | 0.0909002 |
| arousal_20 | 120 | 541 | 2.787431 | 1.9638183 | 2.0 | 2.558892 | 1.4826 | 1 | 9 | 8 | 0.8025363 | -0.3715981 | 0.0844311 |
| arousal_21 | 121 | 541 | 5.255083 | 2.0352443 | 6.0 | 5.330254 | 1.4826 | 1 | 9 | 8 | -0.3667483 | -0.5184239 | 0.0875020 |
| arousal_22 | 122 | 541 | 4.205176 | 2.0038045 | 5.0 | 4.203233 | 1.4826 | 1 | 9 | 8 | -0.0960942 | -0.9906488 | 0.0861503 |
| arousal_23 | 123 | 541 | 4.504621 | 2.3637848 | 5.0 | 4.464203 | 2.9652 | 1 | 9 | 8 | -0.0330572 | -1.1189557 | 0.1016270 |
| arousal_24 | 124 | 540 | 4.890741 | 2.2325612 | 5.0 | 4.953704 | 2.9652 | 1 | 9 | 8 | -0.3464295 | -0.8741986 | 0.0960741 |
| arousal_25 | 125 | 541 | 5.713494 | 2.5322335 | 6.0 | 5.891455 | 2.9652 | 1 | 9 | 8 | -0.6732572 | -0.7422981 | 0.1088692 |
| arousal_26 | 126 | 540 | 3.885185 | 1.8838644 | 4.0 | 3.863426 | 1.4826 | 1 | 9 | 8 | 0.0326009 | -0.8617841 | 0.0810686 |
| arousal_27 | 127 | 539 | 4.083488 | 2.0985227 | 4.0 | 4.011547 | 2.9652 | 1 | 9 | 8 | 0.1500209 | -0.7996554 | 0.0903898 |
| arousal_28 | 128 | 540 | 5.775926 | 2.3018453 | 6.0 | 5.942130 | 1.4826 | 1 | 9 | 8 | -0.6196675 | -0.5140196 | 0.0990556 |
| arousal_29 | 129 | 540 | 6.162963 | 2.6234490 | 7.0 | 6.453704 | 1.4826 | 1 | 9 | 8 | -0.8910572 | -0.4821664 | 0.1128953 |
| arousal_30 | 130 | 539 | 3.487941 | 2.0777953 | 3.0 | 3.334873 | 2.9652 | 1 | 9 | 8 | 0.4228341 | -0.7296508 | 0.0894970 |
| arousal_31 | 131 | 540 | 2.737037 | 1.8945578 | 2.0 | 2.546296 | 1.4826 | 1 | 9 | 8 | 0.7031490 | -0.6894424 | 0.0815288 |
| arousal_32 | 132 | 539 | 2.764378 | 1.8367763 | 2.0 | 2.598152 | 1.4826 | 1 | 9 | 8 | 0.6410206 | -0.8359133 | 0.0791156 |
| arousal_33 | 133 | 540 | 5.537037 | 2.2621592 | 6.0 | 5.678241 | 1.4826 | 1 | 9 | 8 | -0.5386969 | -0.4719313 | 0.0973478 |
| arousal_34 | 134 | 540 | 4.135185 | 2.1857743 | 4.0 | 4.074074 | 2.9652 | 1 | 9 | 8 | 0.1062771 | -0.9682949 | 0.0940608 |
| arousal_35 | 135 | 539 | 4.504638 | 2.1551091 | 5.0 | 4.498845 | 2.9652 | 1 | 9 | 8 | -0.0497018 | -0.8308722 | 0.0928271 |
| arousal_36 | 136 | 540 | 4.062963 | 2.0512267 | 5.0 | 4.032407 | 1.4826 | 1 | 9 | 8 | -0.0447392 | -1.0065924 | 0.0882707 |
| arousal_37 | 137 | 540 | 5.779630 | 2.1548478 | 6.0 | 5.969907 | 1.4826 | 1 | 9 | 8 | -0.7119252 | -0.1416199 | 0.0927299 |
| arousal_38 | 138 | 540 | 6.501852 | 2.7623892 | 8.0 | 6.877315 | 1.4826 | 1 | 9 | 8 | -1.0633254 | -0.2861846 | 0.1188743 |
| arousal_39 | 139 | 540 | 5.407407 | 2.4022139 | 6.0 | 5.523148 | 2.9652 | 1 | 9 | 8 | -0.4176104 | -0.8539825 | 0.1033748 |
| arousal_40 | 140 | 540 | 3.983333 | 1.9723741 | 4.0 | 3.939815 | 1.4826 | 1 | 9 | 8 | 0.1100159 | -0.7196616 | 0.0848775 |
| arousal_41 | 141 | 540 | 6.001852 | 2.2335767 | 6.0 | 6.210648 | 1.4826 | 1 | 9 | 8 | -0.7092867 | -0.2073858 | 0.0961178 |
| arousal_42 | 142 | 540 | 5.161111 | 2.1652438 | 6.0 | 5.289352 | 1.4826 | 1 | 9 | 8 | -0.5543246 | -0.5420969 | 0.0931773 |
| arousal_43 | 143 | 539 | 5.866419 | 2.1985050 | 6.0 | 6.046189 | 1.4826 | 1 | 9 | 8 | -0.7271191 | -0.1288243 | 0.0946963 |
| arousal_44 | 144 | 540 | 2.740741 | 1.8584929 | 2.0 | 2.567130 | 1.4826 | 1 | 8 | 7 | 0.6604586 | -0.8406688 | 0.0799768 |
| arousal_45 | 145 | 539 | 5.482375 | 2.2196246 | 6.0 | 5.660508 | 1.4826 | 1 | 9 | 8 | -0.6464319 | -0.4297823 | 0.0956060 |
| arousal_46 | 146 | 540 | 5.333333 | 2.2323312 | 6.0 | 5.469907 | 1.4826 | 1 | 9 | 8 | -0.4854203 | -0.6285827 | 0.0960642 |
| arousal_47 | 147 | 540 | 6.057407 | 2.1908989 | 6.0 | 6.293982 | 1.4826 | 1 | 9 | 8 | -0.8590956 | 0.1199776 | 0.0942813 |
| arousal_48 | 148 | 539 | 4.051948 | 1.8933190 | 4.0 | 4.048499 | 1.4826 | 1 | 9 | 8 | -0.0121997 | -0.6973739 | 0.0815510 |
| arousal_49 | 149 | 539 | 4.452690 | 2.1030795 | 5.0 | 4.457275 | 1.4826 | 1 | 9 | 8 | -0.1271704 | -0.7716484 | 0.0905860 |
| arousal_50 | 150 | 540 | 6.420370 | 2.5928315 | 7.0 | 6.775463 | 1.4826 | 1 | 9 | 8 | -1.0106337 | -0.1834490 | 0.1115777 |
| arousal_51 | 151 | 534 | 5.310861 | 1.9746978 | 6.0 | 5.422897 | 1.4826 | 1 | 9 | 8 | -0.5458105 | -0.2375294 | 0.0854535 |
| arousal_52 | 152 | 536 | 6.729478 | 2.0651873 | 7.0 | 7.039535 | 1.4826 | 1 | 9 | 8 | -1.1523959 | 0.9090466 | 0.0892025 |
| arousal_53 | 153 | 536 | 6.406716 | 2.6131715 | 7.0 | 6.753488 | 2.2239 | 1 | 9 | 8 | -0.9651883 | -0.3113564 | 0.1128718 |
| arousal_54 | 154 | 536 | 4.843284 | 2.1758384 | 5.0 | 4.909302 | 2.9652 | 1 | 9 | 8 | -0.3088246 | -0.8254924 | 0.0939819 |
| arousal_55 | 155 | 536 | 3.505597 | 1.9527040 | 3.0 | 3.413953 | 2.9652 | 1 | 9 | 8 | 0.2524466 | -0.8443040 | 0.0843440 |
| arousal_56 | 156 | 536 | 3.645522 | 1.9644738 | 4.0 | 3.567442 | 2.9652 | 1 | 9 | 8 | 0.1937946 | -0.8604313 | 0.0848523 |
| arousal_57 | 157 | 535 | 4.041122 | 1.8365287 | 4.0 | 4.041958 | 1.4826 | 1 | 9 | 8 | -0.0006724 | -0.6464555 | 0.0794001 |
| arousal_58 | 158 | 536 | 4.354478 | 1.9843543 | 5.0 | 4.369767 | 1.4826 | 1 | 9 | 8 | -0.0425377 | -0.7342813 | 0.0857111 |
| arousal_59 | 159 | 536 | 4.889925 | 2.1428017 | 5.0 | 4.918605 | 2.9652 | 1 | 9 | 8 | -0.1860362 | -0.8131370 | 0.0925549 |
| arousal_60 | 160 | 535 | 4.472897 | 1.9417341 | 5.0 | 4.505827 | 1.4826 | 1 | 9 | 8 | -0.1352945 | -0.6191260 | 0.0839485 |
| arousal_61 | 161 | 536 | 6.147388 | 2.4946134 | 7.0 | 6.430233 | 1.4826 | 1 | 9 | 8 | -0.8591341 | -0.3763184 | 0.1077509 |
| arousal_62 | 162 | 536 | 4.837687 | 1.8183276 | 5.0 | 4.946512 | 1.4826 | 1 | 9 | 8 | -0.5093604 | -0.3018838 | 0.0785398 |
| arousal_63 | 163 | 536 | 2.707090 | 1.9155112 | 2.0 | 2.551163 | 1.4826 | 1 | 9 | 8 | 0.5710953 | -1.1475852 | 0.0827375 |
| arousal_64 | 164 | 535 | 6.708411 | 2.6712901 | 8.0 | 7.130536 | 1.4826 | 1 | 9 | 8 | -1.2327293 | 0.1540149 | 0.1154899 |
| arousal_65 | 165 | 536 | 4.750000 | 2.1825562 | 5.0 | 4.739535 | 2.9652 | 1 | 9 | 8 | -0.0714652 | -0.8706885 | 0.0942721 |
| arousal_66 | 166 | 535 | 5.474766 | 2.3833887 | 6.0 | 5.620047 | 2.9652 | 1 | 9 | 8 | -0.5113302 | -0.7806575 | 0.1030429 |
| arousal_67 | 167 | 536 | 3.636194 | 1.9962853 | 4.0 | 3.560465 | 2.9652 | 1 | 9 | 8 | 0.1697349 | -1.0624011 | 0.0862264 |
| arousal_68 | 168 | 536 | 3.501866 | 2.0213802 | 3.0 | 3.400000 | 2.9652 | 1 | 9 | 8 | 0.2385400 | -1.0211984 | 0.0873103 |
| arousal_69 | 169 | 536 | 5.511194 | 2.3120703 | 6.0 | 5.686046 | 1.4826 | 1 | 9 | 8 | -0.6568640 | -0.5863677 | 0.0998662 |
| arousal_70 | 170 | 536 | 5.638060 | 2.2213003 | 6.0 | 5.827907 | 1.4826 | 1 | 9 | 8 | -0.6846725 | -0.3213796 | 0.0959456 |
| arousal_71 | 171 | 535 | 3.280374 | 1.9905883 | 3.0 | 3.128205 | 2.9652 | 1 | 9 | 8 | 0.4328206 | -0.7690077 | 0.0860606 |
| arousal_72 | 172 | 535 | 2.970093 | 1.8710897 | 2.0 | 2.864802 | 1.4826 | 1 | 9 | 8 | 0.4593250 | -0.8698084 | 0.0808943 |
| arousal_73 | 173 | 536 | 5.639925 | 2.1406199 | 6.0 | 5.806977 | 1.4826 | 1 | 9 | 8 | -0.6683344 | -0.1705345 | 0.0924607 |
| arousal_74 | 174 | 536 | 4.192164 | 2.0685482 | 5.0 | 4.167442 | 1.4826 | 1 | 9 | 8 | -0.0384711 | -0.9325219 | 0.0893477 |
| arousal_75 | 175 | 536 | 3.576493 | 1.9232293 | 4.0 | 3.497674 | 1.4826 | 1 | 9 | 8 | 0.2029690 | -0.8100441 | 0.0830709 |
| arousal_76 | 176 | 535 | 4.347664 | 2.0578408 | 5.0 | 4.305361 | 2.9652 | 1 | 9 | 8 | 0.1456330 | -0.6650043 | 0.0889682 |
| arousal_77 | 177 | 536 | 5.139925 | 2.0618953 | 5.0 | 5.200000 | 1.4826 | 1 | 9 | 8 | -0.3127026 | -0.3732340 | 0.0890603 |
| arousal_78 | 178 | 536 | 4.602612 | 2.0113493 | 5.0 | 4.658139 | 1.4826 | 1 | 9 | 8 | -0.2185290 | -0.8056102 | 0.0868771 |
| arousal_79 | 179 | 536 | 5.970149 | 2.0340661 | 6.0 | 6.139535 | 1.4826 | 1 | 9 | 8 | -0.6991472 | -0.0100929 | 0.0878583 |
| arousal_80 | 180 | 536 | 4.289179 | 1.9784716 | 5.0 | 4.286047 | 1.4826 | 1 | 9 | 8 | -0.0854583 | -0.7548291 | 0.0854570 |
| arousal_81 | 181 | 536 | 6.425373 | 2.7530832 | 7.5 | 6.776744 | 2.2239 | 1 | 9 | 8 | -1.0247909 | -0.3523735 | 0.1189151 |
| arousal_82 | 182 | 536 | 4.718284 | 1.9786056 | 5.0 | 4.765116 | 1.4826 | 1 | 9 | 8 | -0.1823001 | -0.5932278 | 0.0854627 |
| arousal_83 | 183 | 536 | 3.988806 | 2.2778629 | 4.0 | 3.855814 | 2.9652 | 1 | 9 | 8 | 0.2524418 | -0.8751976 | 0.0983887 |
| arousal_84 | 184 | 535 | 4.641121 | 2.0812590 | 5.0 | 4.675991 | 1.4826 | 1 | 9 | 8 | -0.1216858 | -0.6765756 | 0.0899807 |
| arousal_85 | 185 | 535 | 4.861682 | 2.0036318 | 5.0 | 4.960373 | 1.4826 | 1 | 9 | 8 | -0.3957426 | -0.5721818 | 0.0866246 |
| arousal_86 | 186 | 536 | 4.082090 | 1.8056961 | 5.0 | 4.111628 | 1.4826 | 1 | 9 | 8 | -0.1508005 | -0.6165233 | 0.0779942 |
| arousal_87 | 187 | 536 | 5.330224 | 2.0582864 | 6.0 | 5.462791 | 1.4826 | 1 | 9 | 8 | -0.5930146 | -0.3746321 | 0.0889044 |
| arousal_88 | 188 | 536 | 5.722015 | 1.9904352 | 6.0 | 5.906977 | 1.4826 | 1 | 9 | 8 | -0.7371702 | 0.0783366 | 0.0859737 |
| arousal_89 | 189 | 536 | 6.020522 | 2.2005837 | 6.0 | 6.225581 | 1.4826 | 1 | 9 | 8 | -0.7171924 | -0.1689512 | 0.0950507 |
| arousal_90 | 190 | 535 | 3.009346 | 1.9025674 | 2.0 | 2.890443 | 1.4826 | 1 | 9 | 8 | 0.5028700 | -0.6920294 | 0.0822552 |
| arousal_91 | 191 | 536 | 4.908582 | 1.9491290 | 5.0 | 4.983721 | 1.4826 | 1 | 9 | 8 | -0.3253081 | -0.4190262 | 0.0841895 |
| arousal_92 | 192 | 536 | 4.897388 | 1.8175450 | 5.0 | 5.025581 | 1.4826 | 1 | 9 | 8 | -0.5600445 | -0.0729064 | 0.0785060 |
| arousal_93 | 193 | 536 | 4.283582 | 2.0456909 | 5.0 | 4.248837 | 1.4826 | 1 | 9 | 8 | 0.0834765 | -0.6252435 | 0.0883604 |
| arousal_94 | 194 | 536 | 5.662313 | 2.3473504 | 6.0 | 5.860465 | 1.4826 | 1 | 9 | 8 | -0.6923747 | -0.5232144 | 0.1013901 |
| arousal_95 | 195 | 536 | 5.617537 | 2.3236272 | 6.0 | 5.795349 | 1.4826 | 1 | 9 | 8 | -0.6127793 | -0.6056714 | 0.1003654 |
| arousal_96 | 196 | 536 | 4.891791 | 2.0921410 | 5.0 | 4.967442 | 1.4826 | 1 | 9 | 8 | -0.3457631 | -0.6386402 | 0.0903667 |
| arousal_97 | 197 | 536 | 5.343284 | 1.9806710 | 6.0 | 5.483721 | 1.4826 | 1 | 9 | 8 | -0.6327133 | -0.2107756 | 0.0855520 |
| arousal_98 | 198 | 535 | 5.801869 | 2.0558668 | 6.0 | 5.946387 | 1.4826 | 1 | 9 | 8 | -0.5941985 | -0.0869923 | 0.0888829 |
| arousal_99 | 199 | 536 | 5.675373 | 1.9866446 | 6.0 | 5.881395 | 1.4826 | 1 | 9 | 8 | -0.8155053 | 0.1591686 | 0.0858100 |
| arousal_100 | 200 | 535 | 4.585047 | 2.2518741 | 5.0 | 4.552448 | 2.9652 | 1 | 9 | 8 | 0.0306351 | -0.9774071 | 0.0973570 |
#Stats
image_ratings_all %>%
select(valence_human, valence_ml, adjusted_valence) %>%
tbl_summary(
label = list(valence_human ~"Human", valence_ml ~ "Machine", adjusted_valence ~ "OASIS Adjusted"),
statistic = list(all_continuous() ~ "{min}-{max} ({mean}, {SD})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Valence") | Valence | |
| N = 1001 | |
|---|---|
| Human | 1.48-8.03 (4.75, 1.31) |
| Machine | 3.66-7.15 (5.14, 0.95) |
| OASIS Adjusted | 2.01-8.05 (4.73, 2.08) |
| Unknown | 88 |
| 1 Minimum-Maximum (Mean, SD) | |
image_ratings_all %>%
select(arousal_human, arousal_ml, adjusted_arousal) %>%
tbl_summary(label = list(arousal_human ~"Human", arousal_ml ~ "Machine",
adjusted_arousal ~ "OASIS Adjusted"),
statistic = list(all_continuous() ~ "{min}-{max} ({mean}, {SD})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Arousal") | Arousal | |
| N = 1001 | |
|---|---|
| Human | 2.70-6.73 (4.77, 1.06) |
| Machine | 3.86-6.24 (4.85, 0.72) |
| OASIS Adjusted | 3.87-6.33 (4.97, 0.82) |
| Unknown | 88 |
| 1 Minimum-Maximum (Mean, SD) | |
#Stats by Source
image_ratings_all %>%
select(source, valence_human, valence_ml) %>%
tbl_summary(by = source,
label = list(valence_human ~"Human", valence_ml ~ "Machine"),
statistic = list(all_continuous() ~ "{min}-{max} ({mean}, {SD})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Valence") | Valence | ||
| OASIS, N = 121 | smartphone, N = 881 | |
|---|---|---|
| Human | 2.00-8.03 (4.68, 1.98) | 1.48-7.38 (4.76, 1.20) |
| Machine | 3.78-7.15 (5.38, 1.24) | 3.66-6.99 (5.11, 0.91) |
| 1 Minimum-Maximum (Mean, SD) | ||
image_ratings_all %>%
select(source, arousal_human, arousal_ml) %>%
tbl_summary(by = source,
label = list(arousal_human ~"Human", arousal_ml ~ "Machine"),
statistic = list(all_continuous() ~ "{min}-{max} ({mean}, {SD})"),
missing = "ifany") %>%
modify_header(label ~ "") %>%
as_gt() %>%
tab_header("Arousal") | Arousal | ||
| OASIS, N = 121 | smartphone, N = 881 | |
|---|---|---|
| Human | 4.03-6.73 (5.32, 0.89) | 2.70-6.71 (4.70, 1.07) |
| Machine | 4.13-6.24 (5.18, 0.64) | 3.86-6.03 (4.80, 0.73) |
| 1 Minimum-Maximum (Mean, SD) | ||
Correlation Tests
Let’s look at the correlation between the machine and human ratings for the 100 images.
##
## Pearson's product-moment correlation
##
## data: image_ratings_all$valence_human and image_ratings_all$valence_ml
## t = 4.4837, df = 98, p-value = 1.995e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2352184 0.5633416
## sample estimates:
## cor
## 0.4125751
##
## Pearson's product-moment correlation
##
## data: image_ratings_all$arousal_human and image_ratings_all$arousal_ml
## t = 5.5485, df = 98, p-value = 2.453e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3235776 0.6252911
## sample estimates:
## cor
## 0.4889216
# Smartphone Only
cor.test(image_ratings_smartphone$valence_human, image_ratings_smartphone$valence_ml)##
## Pearson's product-moment correlation
##
## data: image_ratings_smartphone$valence_human and image_ratings_smartphone$valence_ml
## t = 2.6429, df = 86, p-value = 0.009766
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0685738 0.4572727
## sample estimates:
## cor
## 0.2740797
##
## Pearson's product-moment correlation
##
## data: image_ratings_smartphone$arousal_human and image_ratings_smartphone$arousal_ml
## t = 4.999, df = 86, p-value = 3.004e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2943159 0.6221228
## sample estimates:
## cor
## 0.4745084
##
## Pearson's product-moment correlation
##
## data: image_ratings_smartphone$arousal_human and image_ratings_smartphone$valence_human
## t = -3.2914, df = 86, p-value = 0.001447
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5083133 -0.1344577
## sample estimates:
## cor
## -0.3344806
##
## Pearson's product-moment correlation
##
## data: image_ratings_smartphone$arousal_ml and image_ratings_smartphone$valence_ml
## t = 4.5787, df = 86, p-value = 1.567e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2571107 0.5968167
## sample estimates:
## cor
## 0.4427129
##
## Pearson's product-moment correlation
##
## data: image_ratings_OASIS$valence_human and image_ratings_OASIS$valence_ml
## t = 7.3693, df = 10, p-value = 2.399e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7301304 0.9773935
## sample estimates:
## cor
## 0.9189647
##
## Pearson's product-moment correlation
##
## data: image_ratings_OASIS$arousal_human and image_ratings_OASIS$arousal_ml
## t = 1.5694, df = 10, p-value = 0.1476
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1736468 0.8114358
## sample estimates:
## cor
## 0.4445578
##
## Pearson's product-moment correlation
##
## data: image_ratings_OASIS$arousal_human and image_ratings_OASIS$valence_human
## t = -0.3572, df = 10, p-value = 0.7284
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6446201 0.4934450
## sample estimates:
## cor
## -0.1122424
##
## Pearson's product-moment correlation
##
## data: image_ratings_OASIS$arousal_ml and image_ratings_OASIS$valence_ml
## t = -0.67258, df = 10, p-value = 0.5165
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6985377 0.4154695
## sample estimates:
## cor
## -0.208036
#Adjusted Arousal vs Machine Arousal
cor.test(image_ratings_OASIS$adjusted_arousal, image_ratings_OASIS$arousal_ml)##
## Pearson's product-moment correlation
##
## data: image_ratings_OASIS$adjusted_arousal and image_ratings_OASIS$arousal_ml
## t = 1.2951, df = 10, p-value = 0.2244
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2490919 0.7826595
## sample estimates:
## cor
## 0.3789876
#Adjusted Valence vs Machine Valence
cor.test(image_ratings_OASIS$adjusted_valence, image_ratings_OASIS$valence_ml)##
## Pearson's product-moment correlation
##
## data: image_ratings_OASIS$adjusted_valence and image_ratings_OASIS$valence_ml
## t = 9.1492, df = 10, p-value = 3.569e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.811295 0.984844
## sample estimates:
## cor
## 0.9451373
#Adjusted Arousal vs Human
cor.test(image_ratings_OASIS$adjusted_arousal, image_ratings_OASIS$arousal_human)##
## Pearson's product-moment correlation
##
## data: image_ratings_OASIS$adjusted_arousal and image_ratings_OASIS$arousal_human
## t = 5.3868, df = 10, p-value = 0.0003071
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5711619 0.9607748
## sample estimates:
## cor
## 0.8623825
#Adjusted Valence vs Human
cor.test(image_ratings_OASIS$adjusted_valence, image_ratings_OASIS$valence_human)##
## Pearson's product-moment correlation
##
## data: image_ratings_OASIS$adjusted_valence and image_ratings_OASIS$valence_human
## t = 15.806, df = 10, p-value = 2.113e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9300490 0.9947013
## sample estimates:
## cor
## 0.9805667
ICC
## Call: psych::ICC(x = image_ratings_all[, c("valence_human", "valence_ml")])
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.36 2.1 99 100 1.2e-04 0.17 0.52
## Single_random_raters ICC2 0.37 2.3 99 99 2.4e-05 0.19 0.53
## Single_fixed_raters ICC3 0.39 2.3 99 99 2.4e-05 0.21 0.55
## Average_raters_absolute ICC1k 0.53 2.1 99 100 1.2e-04 0.30 0.68
## Average_random_raters ICC2k 0.54 2.3 99 99 2.4e-05 0.32 0.69
## Average_fixed_raters ICC3k 0.56 2.3 99 99 2.4e-05 0.35 0.71
##
## Number of subjects = 100 Number of Judges = 2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
## Call: psych::ICC(x = image_ratings_all[, c("arousal_human", "arousal_ml")])
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.46 2.7 99 100 7.6e-07 0.29 0.60
## Single_random_raters ICC2 0.46 2.7 99 99 8.2e-07 0.29 0.60
## Single_fixed_raters ICC3 0.46 2.7 99 99 8.2e-07 0.29 0.60
## Average_raters_absolute ICC1k 0.63 2.7 99 100 7.6e-07 0.45 0.75
## Average_random_raters ICC2k 0.63 2.7 99 99 8.2e-07 0.45 0.75
## Average_fixed_raters ICC3k 0.63 2.7 99 99 8.2e-07 0.44 0.75
##
## Number of subjects = 100 Number of Judges = 2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
## Call: psych::ICC(x = image_ratings_smartphone[, c("valence_human",
## "valence_ml")])
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.23 1.6 87 88 0.0132 0.028 0.42
## Single_random_raters ICC2 0.25 1.7 87 87 0.0063 0.054 0.43
## Single_fixed_raters ICC3 0.26 1.7 87 87 0.0063 0.058 0.45
## Average_raters_absolute ICC1k 0.38 1.6 87 88 0.0132 0.055 0.59
## Average_random_raters ICC2k 0.40 1.7 87 87 0.0063 0.103 0.60
## Average_fixed_raters ICC3k 0.42 1.7 87 87 0.0063 0.111 0.62
##
## Number of subjects = 88 Number of Judges = 2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
## Call: psych::ICC(x = image_ratings_smartphone[, c("arousal_human",
## "arousal_ml")])
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.44 2.6 87 88 7.1e-06 0.26 0.59
## Single_random_raters ICC2 0.44 2.6 87 87 7.6e-06 0.26 0.59
## Single_fixed_raters ICC3 0.44 2.6 87 87 7.6e-06 0.26 0.59
## Average_raters_absolute ICC1k 0.61 2.6 87 88 7.1e-06 0.41 0.75
## Average_random_raters ICC2k 0.61 2.6 87 87 7.6e-06 0.41 0.75
## Average_fixed_raters ICC3k 0.61 2.6 87 87 7.6e-06 0.41 0.75
##
## Number of subjects = 88 Number of Judges = 2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
## Call: psych::ICC(x = image_ratings_OASIS[, c("valence_human", "valence_ml")])
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.76 7.4 11 12 0.00082 0.38 0.92
## Single_random_raters ICC2 0.77 10.6 11 11 0.00024 0.30 0.93
## Single_fixed_raters ICC3 0.83 10.6 11 11 0.00024 0.51 0.95
## Average_raters_absolute ICC1k 0.87 7.4 11 12 0.00082 0.55 0.96
## Average_random_raters ICC2k 0.87 10.6 11 11 0.00024 0.46 0.96
## Average_fixed_raters ICC3k 0.91 10.6 11 11 0.00024 0.67 0.97
##
## Number of subjects = 12 Number of Judges = 2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
## Call: psych::ICC(x = image_ratings_OASIS[, c("arousal_human", "arousal_ml")])
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.44 2.6 11 12 0.058 -0.12 0.80
## Single_random_raters ICC2 0.44 2.6 11 11 0.065 -0.13 0.80
## Single_fixed_raters ICC3 0.44 2.6 11 11 0.065 -0.15 0.80
## Average_raters_absolute ICC1k 0.61 2.6 11 12 0.058 -0.28 0.89
## Average_random_raters ICC2k 0.61 2.6 11 11 0.065 -0.29 0.89
## Average_fixed_raters ICC3k 0.61 2.6 11 11 0.065 -0.34 0.89
##
## Number of subjects = 12 Number of Judges = 2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
## Call: psych::ICC(x = image_ratings_OASIS[, c("valence_human", "adjusted_valence")])
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.98 104 11 12 3.9e-10 0.94 0.99
## Single_random_raters ICC2 0.98 104 11 11 1.8e-09 0.94 0.99
## Single_fixed_raters ICC3 0.98 104 11 11 1.8e-09 0.94 0.99
## Average_raters_absolute ICC1k 0.99 104 11 12 3.9e-10 0.97 1.00
## Average_random_raters ICC2k 0.99 104 11 11 1.8e-09 0.97 1.00
## Average_fixed_raters ICC3k 0.99 104 11 11 1.8e-09 0.97 1.00
##
## Number of subjects = 12 Number of Judges = 2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
## Call: psych::ICC(x = image_ratings_OASIS[, c("arousal_human", "adjusted_arousal")])
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.79 8.7 11 12 4.0e-04 0.45 0.93
## Single_random_raters ICC2 0.80 13.2 11 11 8.4e-05 0.31 0.94
## Single_fixed_raters ICC3 0.86 13.2 11 11 8.4e-05 0.58 0.96
## Average_raters_absolute ICC1k 0.88 8.7 11 12 4.0e-04 0.62 0.97
## Average_random_raters ICC2k 0.89 13.2 11 11 8.4e-05 0.47 0.97
## Average_fixed_raters ICC3k 0.92 13.2 11 11 8.4e-05 0.74 0.98
##
## Number of subjects = 12 Number of Judges = 2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
Lin’s Correspondance Coefficient
#All Images Valence ICC
CCC(image_ratings_all$valence_human, image_ratings_all$valence_ml,
ci = "z-transform", conf.level = 0.95, na.rm = FALSE)## $rho.c
## est lwr.ci upr.ci
## 1 0.3701988 0.2085245 0.5121785
##
## $s.shift
## [1] 0.7280459
##
## $l.shift
## [1] 0.3568649
##
## $C.b
## [1] 0.8972882
##
## $blalt
## mean delta
## 1 4.231094 -1.76348145
## 2 5.010819 1.91921314
## 3 7.012616 -0.08866542
## 4 5.574274 0.95496401
## 5 5.831826 -0.24036190
## 6 5.134150 -0.60533612
## 7 5.307320 -1.36510305
## 8 5.808079 -0.88048266
## 9 5.806914 -0.84488180
## 10 3.757827 -3.44541445
## 11 5.714039 -0.51865151
## 12 5.478971 1.79621748
## 13 4.453889 -1.65084590
## 14 4.964480 -1.28940281
## 15 5.761132 1.96017546
## 16 4.941599 0.25013567
## 17 3.590095 -0.35796719
## 18 3.764079 -0.51152227
## 19 4.223812 -1.94854841
## 20 5.030330 -0.43843789
## 21 5.410856 0.48939836
## 22 6.232377 2.29080218
## 23 2.989497 -1.74195608
## 24 7.183542 0.07447522
## 25 4.536822 1.74857781
## 26 4.127332 0.75274330
## 27 5.093996 -0.18428865
## 28 4.422665 -0.99690098
## 29 4.510587 -0.66561892
## 30 5.491943 1.12722606
## 31 4.444251 1.52554553
## 32 6.260963 1.37066660
## 33 2.902122 -2.75609593
## 34 4.285512 -2.00170746
## 35 5.989656 -0.56079414
## 36 6.122158 -0.97024226
## 37 5.574306 -1.17079387
## 38 4.781054 -0.91395993
## 39 5.285436 -1.24865029
## 40 5.699477 -1.34719750
## 41 4.150373 -0.74972640
## 42 4.967931 -1.26178696
## 43 3.826475 0.21742018
## 44 5.358619 -1.01668274
## 45 4.914971 -0.51142414
## 46 5.247406 -1.14666423
## 47 2.956800 -1.90989615
## 48 4.701994 0.27077858
## 49 7.414296 1.23110875
## 50 2.954445 -1.64769608
## 51 3.434404 -0.77925568
## 52 5.105919 -0.66706224
## 53 4.965517 0.21849936
## 54 4.583227 1.79623268
## 55 5.503869 0.43629192
## 56 5.782253 1.40745731
## 57 4.211114 1.04172842
## 58 5.713391 -1.66558799
## 59 3.668289 -2.95224882
## 60 6.281572 -1.42508415
## 61 4.939851 -0.38716564
## 62 2.708492 -2.05131168
## 63 5.624503 0.09427779
## 64 3.223345 -1.19295803
## 65 3.986705 0.41091848
## 66 4.901721 -0.07582983
## 67 3.350918 -0.79546798
## 68 5.040710 -0.59897954
## 69 4.025206 -1.17727724
## 70 5.332566 -0.15020731
## 71 4.344186 1.10640436
## 72 4.108419 -0.45191271
## 73 3.883077 0.27862266
## 74 5.640004 -0.09344142
## 75 5.474637 1.33162584
## 76 5.549609 1.43436504
## 77 5.284225 0.67781893
## 78 4.633546 1.91574441
## 79 4.952772 -0.77800321
## 80 6.120279 -0.40100658
## 81 3.179912 -3.39160025
## 82 5.712910 0.41373212
## 83 4.317287 1.25721682
## 84 4.909296 -1.98650322
## 85 5.495317 -0.57272278
## 86 6.039788 -1.84450219
## 87 5.024727 -1.61288602
## 88 5.487065 -1.03405455
## 89 6.071973 -1.25839227
## 90 4.183344 0.99375611
## 91 5.867570 -1.63066159
## 92 5.797201 1.02500019
## 93 5.754038 -0.19091091
## 94 5.536918 0.65004390
## 95 5.127951 -2.07306659
## 96 4.622624 -2.07733678
## 97 5.432084 -1.62536202
## 98 4.534335 -0.48284955
## 99 4.981384 0.45887290
## 100 4.779010 -0.33414040
#All Images Arousal ICC
CCC(image_ratings_all$arousal_human, image_ratings_all$arousal_ml,
ci = "z-transform", conf.level = 0.95, na.rm = FALSE)## $rho.c
## est lwr.ci upr.ci
## 1 0.453063 0.2993062 0.5838856
##
## $s.shift
## [1] 0.6797606
##
## $l.shift
## [1] 0.08618118
##
## $C.b
## [1] 0.9266579
##
## $blalt
## mean delta
## 1 5.084842 1.072459489
## 2 4.997177 -0.090649548
## 3 4.860327 -0.550560971
## 4 4.086234 0.334001750
## 5 4.656127 -1.371513330
## 6 5.894015 0.123080556
## 7 5.786730 0.215818956
## 8 5.539622 -0.401467111
## 9 5.413266 -0.737807708
## 10 6.038216 0.595181650
## 11 5.670027 0.452921985
## 12 5.274936 -0.894316222
## 13 4.530509 -0.998170710
## 14 3.325194 -1.075525816
## 15 5.258760 -0.007352821
## 16 4.129794 0.150764201
## 17 4.379955 0.249333072
## 18 4.703859 0.373764341
## 19 4.930257 1.566473230
## 20 4.215534 -0.660697815
## 21 4.266440 -0.365904359
## 22 4.890584 1.770684526
## 23 5.365189 1.595547963
## 24 4.732736 1.203658619
## 25 3.898402 -0.820923369
## 26 3.483254 -1.492433263
## 27 3.334166 -1.139574121
## 28 4.790888 1.492298037
## 29 4.052831 0.164708185
## 30 4.426297 0.156683419
## 31 4.289259 -0.452591437
## 32 5.679035 0.201188930
## 33 5.382810 2.238084152
## 34 4.912491 0.989832007
## 35 4.417338 -0.634675400
## 36 4.832669 -1.698671667
## 37 5.775212 0.453279352
## 38 5.516847 -0.711471889
## 39 5.747799 0.237240795
## 40 3.305791 -1.130100959
## 41 5.677584 -0.390419232
## 42 5.634107 -0.601547367
## 43 5.572210 0.970394207
## 44 4.876308 -1.648719948
## 45 4.425896 0.053588867
## 46 5.067772 -0.509619037
## 47 5.951270 0.938199770
## 48 5.773427 -0.925131977
## 49 6.327227 0.804502212
## 50 5.964545 0.884342418
## 51 4.605823 0.474921582
## 52 3.684059 -0.356924185
## 53 4.026997 -0.762949612
## 54 4.163418 -0.244593505
## 55 4.139998 0.428960012
## 56 5.018396 -0.256940627
## 57 3.516212 -1.623920314
## 58 5.141555 -1.337316404
## 59 5.960067 0.374642060
## 60 5.255333 -0.835293733
## 61 3.294183 -1.174187748
## 62 5.523669 2.369483915
## 63 4.423854 0.652292300
## 64 4.945777 1.057979355
## 65 3.936512 -0.600636670
## 66 3.709776 -0.415820328
## 67 4.900508 1.221371430
## 68 3.839682 -1.431215926
## 69 5.113746 1.048627001
## 70 3.593448 -0.626149168
## 71 3.615326 -1.290464542
## 72 5.079920 1.120011673
## 73 4.234177 -0.084026621
## 74 4.378034 -1.603083463
## 75 4.249208 0.196910551
## 76 4.761062 0.757726073
## 77 4.590446 0.024331940
## 78 5.185400 1.569498254
## 79 3.391183 -0.967208042
## 80 4.283544 0.011269304
## 81 5.306970 2.236807134
## 82 4.596331 0.243904982
## 83 4.213116 -0.448619630
## 84 5.200466 -1.118689505
## 85 5.332045 -0.940725057
## 86 5.021809 -1.879439748
## 87 5.537033 -0.413618119
## 88 5.531641 0.380748325
## 89 5.985854 0.069337688
## 90 3.519540 -1.578821610
## 91 3.432870 -0.847048706
## 92 5.080882 -0.344598910
## 93 5.023493 -0.252209940
## 94 4.222080 0.123004090
## 95 5.730839 -0.137050567
## 96 5.150712 0.933649813
## 97 5.140310 -0.497037255
## 98 5.588330 -0.490091918
## 99 5.918260 -0.232780841
## 100 5.678478 -0.006209866
#Smartphone Valence ICC
CCC(image_ratings_smartphone$valence_human, image_ratings_smartphone$valence_ml,
ci = "z-transform", conf.level = 0.95, na.rm = FALSE)## $rho.c
## est lwr.ci upr.ci
## 1 0.2496355 0.06248923 0.4198232
##
## $s.shift
## [1] 0.7558816
##
## $l.shift
## [1] 0.3420502
##
## $C.b
## [1] 0.9108138
##
## $blalt
## mean delta
## 1 5.010819 1.91921314
## 2 5.574274 0.95496401
## 3 5.831826 -0.24036190
## 4 5.134150 -0.60533612
## 5 5.307320 -1.36510305
## 6 5.808079 -0.88048266
## 7 5.806914 -0.84488180
## 8 3.757827 -3.44541445
## 9 5.714039 -0.51865151
## 10 5.478971 1.79621748
## 11 4.964480 -1.28940281
## 12 5.761132 1.96017546
## 13 4.941599 0.25013567
## 14 3.590095 -0.35796719
## 15 3.764079 -0.51152227
## 16 4.223812 -1.94854841
## 17 5.030330 -0.43843789
## 18 5.410856 0.48939836
## 19 6.232377 2.29080218
## 20 2.989497 -1.74195608
## 21 4.536822 1.74857781
## 22 4.127332 0.75274330
## 23 5.093996 -0.18428865
## 24 4.422665 -0.99690098
## 25 4.510587 -0.66561892
## 26 5.491943 1.12722606
## 27 4.444251 1.52554553
## 28 6.260963 1.37066660
## 29 2.902122 -2.75609593
## 30 4.285512 -2.00170746
## 31 6.122158 -0.97024226
## 32 5.574306 -1.17079387
## 33 4.781054 -0.91395993
## 34 5.285436 -1.24865029
## 35 5.699477 -1.34719750
## 36 4.150373 -0.74972640
## 37 4.967931 -1.26178696
## 38 3.826475 0.21742018
## 39 5.358619 -1.01668274
## 40 4.914971 -0.51142414
## 41 5.105919 -0.66706224
## 42 4.965517 0.21849936
## 43 4.583227 1.79623268
## 44 5.503869 0.43629192
## 45 5.782253 1.40745731
## 46 4.211114 1.04172842
## 47 5.713391 -1.66558799
## 48 3.668289 -2.95224882
## 49 6.281572 -1.42508415
## 50 4.939851 -0.38716564
## 51 2.708492 -2.05131168
## 52 5.624503 0.09427779
## 53 3.223345 -1.19295803
## 54 3.986705 0.41091848
## 55 4.901721 -0.07582983
## 56 3.350918 -0.79546798
## 57 5.040710 -0.59897954
## 58 4.025206 -1.17727724
## 59 5.332566 -0.15020731
## 60 4.344186 1.10640436
## 61 4.108419 -0.45191271
## 62 3.883077 0.27862266
## 63 5.640004 -0.09344142
## 64 5.474637 1.33162584
## 65 5.549609 1.43436504
## 66 5.284225 0.67781893
## 67 4.633546 1.91574441
## 68 4.952772 -0.77800321
## 69 6.120279 -0.40100658
## 70 3.179912 -3.39160025
## 71 5.712910 0.41373212
## 72 4.317287 1.25721682
## 73 4.909296 -1.98650322
## 74 5.495317 -0.57272278
## 75 6.039788 -1.84450219
## 76 5.024727 -1.61288602
## 77 5.487065 -1.03405455
## 78 6.071973 -1.25839227
## 79 4.183344 0.99375611
## 80 5.867570 -1.63066159
## 81 5.797201 1.02500019
## 82 5.754038 -0.19091091
## 83 5.536918 0.65004390
## 84 5.127951 -2.07306659
## 85 4.622624 -2.07733678
## 86 5.432084 -1.62536202
## 87 4.534335 -0.48284955
## 88 4.981384 0.45887290
#Smartphone Arousal ICC
CCC(image_ratings_smartphone$arousal_human, image_ratings_smartphone$arousal_ml,
ci = "z-transform", conf.level = 0.95, na.rm = FALSE)## $rho.c
## est lwr.ci upr.ci
## 1 0.4381846 0.2713722 0.5794399
##
## $s.shift
## [1] 0.6794042
##
## $l.shift
## [1] 0.1204585
##
## $C.b
## [1] 0.9234496
##
## $blalt
## mean delta
## 1 4.997177 -0.090649548
## 2 4.086234 0.334001750
## 3 4.656127 -1.371513330
## 4 5.894015 0.123080556
## 5 5.786730 0.215818956
## 6 5.539622 -0.401467111
## 7 5.413266 -0.737807708
## 8 6.038216 0.595181650
## 9 5.670027 0.452921985
## 10 5.274936 -0.894316222
## 11 3.325194 -1.075525816
## 12 5.258760 -0.007352821
## 13 4.129794 0.150764201
## 14 4.379955 0.249333072
## 15 4.703859 0.373764341
## 16 4.930257 1.566473230
## 17 4.215534 -0.660697815
## 18 4.266440 -0.365904359
## 19 4.890584 1.770684526
## 20 5.365189 1.595547963
## 21 3.898402 -0.820923369
## 22 3.483254 -1.492433263
## 23 3.334166 -1.139574121
## 24 4.790888 1.492298037
## 25 4.052831 0.164708185
## 26 4.426297 0.156683419
## 27 4.289259 -0.452591437
## 28 5.679035 0.201188930
## 29 5.382810 2.238084152
## 30 4.912491 0.989832007
## 31 4.832669 -1.698671667
## 32 5.775212 0.453279352
## 33 5.516847 -0.711471889
## 34 5.747799 0.237240795
## 35 3.305791 -1.130100959
## 36 5.677584 -0.390419232
## 37 5.634107 -0.601547367
## 38 5.572210 0.970394207
## 39 4.876308 -1.648719948
## 40 4.425896 0.053588867
## 41 3.684059 -0.356924185
## 42 4.026997 -0.762949612
## 43 4.163418 -0.244593505
## 44 4.139998 0.428960012
## 45 5.018396 -0.256940627
## 46 3.516212 -1.623920314
## 47 5.141555 -1.337316404
## 48 5.960067 0.374642060
## 49 5.255333 -0.835293733
## 50 3.294183 -1.174187748
## 51 5.523669 2.369483915
## 52 4.423854 0.652292300
## 53 4.945777 1.057979355
## 54 3.936512 -0.600636670
## 55 3.709776 -0.415820328
## 56 4.900508 1.221371430
## 57 3.839682 -1.431215926
## 58 5.113746 1.048627001
## 59 3.593448 -0.626149168
## 60 3.615326 -1.290464542
## 61 5.079920 1.120011673
## 62 4.234177 -0.084026621
## 63 4.378034 -1.603083463
## 64 4.249208 0.196910551
## 65 4.761062 0.757726073
## 66 4.590446 0.024331940
## 67 5.185400 1.569498254
## 68 3.391183 -0.967208042
## 69 4.283544 0.011269304
## 70 5.306970 2.236807134
## 71 4.596331 0.243904982
## 72 4.213116 -0.448619630
## 73 5.200466 -1.118689505
## 74 5.332045 -0.940725057
## 75 5.021809 -1.879439748
## 76 5.537033 -0.413618119
## 77 5.531641 0.380748325
## 78 5.985854 0.069337688
## 79 3.519540 -1.578821610
## 80 3.432870 -0.847048706
## 81 5.080882 -0.344598910
## 82 5.023493 -0.252209940
## 83 4.222080 0.123004090
## 84 5.730839 -0.137050567
## 85 5.150712 0.933649813
## 86 5.140310 -0.497037255
## 87 5.588330 -0.490091918
## 88 5.918260 -0.232780841
#OASIS Valence ICC
CCC(image_ratings_OASIS$valence_human, image_ratings_OASIS$valence_ml,
ci = "z-transform", conf.level = 0.95, na.rm = FALSE)## $rho.c
## est lwr.ci upr.ci
## 1 0.755242 0.5110502 0.8866293
##
## $s.shift
## [1] 0.6272227
##
## $l.shift
## [1] 0.4604461
##
## $C.b
## [1] 0.8218401
##
## $blalt
## mean delta
## 1 4.231094 -1.76348145
## 2 7.012616 -0.08866542
## 3 4.453889 -1.65084590
## 4 7.183542 0.07447522
## 5 5.989656 -0.56079414
## 6 5.247406 -1.14666423
## 7 2.956800 -1.90989615
## 8 4.701994 0.27077858
## 9 7.414296 1.23110875
## 10 2.954445 -1.64769608
## 11 3.434404 -0.77925568
## 12 4.779010 -0.33414040
#OASIS Arousal ICC
CCC(image_ratings_OASIS$arousal_human, image_ratings_OASIS$arousal_ml,
ci = "z-transform", conf.level = 0.95, na.rm = FALSE)## $rho.c
## est lwr.ci upr.ci
## 1 0.4134235 -0.1302138 0.7659374
##
## $s.shift
## [1] 0.7188528
##
## $l.shift
## [1] -0.20164
##
## $C.b
## [1] 0.9299657
##
## $blalt
## mean delta
## 1 5.084842 1.072459489
## 2 4.860327 -0.550560971
## 3 4.530509 -0.998170710
## 4 4.732736 1.203658619
## 5 4.417338 -0.634675400
## 6 5.067772 -0.509619037
## 7 5.951270 0.938199770
## 8 5.773427 -0.925131977
## 9 6.327227 0.804502212
## 10 5.964545 0.884342418
## 11 4.605823 0.474921582
## 12 5.678478 -0.006209866
#OASIS Human Valence ICC
CCC(image_ratings_OASIS$valence_human, image_ratings_OASIS$adjusted_valence,
ci = "z-transform", conf.level = 0.95, na.rm = FALSE)## $rho.c
## est lwr.ci upr.ci
## 1 0.9791759 0.9321853 0.9937115
##
## $s.shift
## [1] 1.048807
##
## $l.shift
## [1] 0.02386034
##
## $C.b
## [1] 0.9985817
##
## $blalt
## mean delta
## 1 3.488402 -0.278097930
## 2 6.977606 -0.018644522
## 3 3.817501 -0.378070144
## 4 7.371116 -0.300672924
## 5 6.150926 -0.883333333
## 6 4.620988 0.106172839
## 7 2.007099 -0.010493827
## 8 4.684124 0.306518981
## 9 8.039678 -0.019654205
## 10 2.083817 0.093559978
## 11 3.045490 -0.001428501
## 12 4.198127 0.827626574
#OASIS Human Arousal ICC
CCC(image_ratings_OASIS$arousal_human, image_ratings_OASIS$adjusted_arousal,
ci = "z-transform", conf.level = 0.95, na.rm = FALSE)## $rho.c
## est lwr.ci upr.ci
## 1 0.7856143 0.4656692 0.923927
##
## $s.shift
## [1] 0.9158766
##
## $l.shift
## [1] -0.4332529
##
## $C.b
## [1] 0.9109813
##
## $blalt
## mean delta
## 1 5.475553 0.29103909
## 2 4.554900 0.06029425
## 3 4.040464 -0.01808166
## 4 5.219063 0.23100575
## 5 3.998718 0.20256410
## 6 4.848789 -0.07165242
## 7 6.376852 0.08703704
## 8 4.899020 0.82368194
## 9 6.136583 1.18578829
## 10 6.049512 0.71440873
## 11 4.358535 0.96949717
## 12 5.786531 -0.22231664
Means and SDs
| Raiting | Mean | SD |
|---|---|---|
| Arousal | ||
| Human | 4.771561 | 1.0642441 |
| Machine | 4.846801 | 0.7234312 |
| OASIS Adjusted | 4.968157 | 0.8177350 |
| Valence | ||
| Human | 4.746738 | 1.3064268 |
| Machine | 5.142547 | 0.9511387 |
| OASIS Adjusted | 4.730261 | 2.0790387 |
| Raiting | Mean | SD |
|---|---|---|
| Arousal | ||
| OASIS Human | 5.322596 | 0.8928440 |
| OASIS Machine | 5.176453 | 0.6418234 |
| Smartphone Human | 4.696420 | 1.0679205 |
| Smartphone Machine | 4.801849 | 0.7255497 |
| Valence | ||
| OASIS Human | 4.683884 | 1.9822890 |
| OASIS Machine | 5.375974 | 1.2433367 |
| Smartphone Human | 4.755309 | 1.2019634 |
| Smartphone Machine | 5.110716 | 0.9085421 |
Day In the Life Deep Affect Module
Data Overview
## Rows: 1,151
## Columns: 6
## $ date_char <dbl> 2.02108e+13, 2.02108e+13, 2.02108e+13, 2.02108e+13, 2.02108…
## $ name_image <chr> NA, NA, NA, NA, "DITL_va6", NA, NA, NA, NA, NA, NA, NA, NA,…
## $ date <chr> "2021-08-12 21:28:11", "2021-08-12 21:28:16", "2021-08-12 2…
## $ valence <dbl> 5.354269, 5.348342, 5.348478, 3.687168, 3.684448, 3.684448,…
## $ arousal <dbl> 4.459608, 4.460746, 4.451868, 4.321830, 4.337230, 4.337230,…
## $ row_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
kbl(describe(image_ratings_DITL[,c(2,3:4)])) %>%
kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| name_image* | 1 | 88 | 44.500000 | 25.5473417 | 44.50000 | 44.500000 | 32.6172000 | 1.000000 | 88.000000 | 87.000000 | 0.0000000 | -1.240980 | 2.7233558 |
| date* | 2 | 1151 | 575.345786 | 332.0175040 | 575.00000 | 575.307275 | 425.5062000 | 1.000000 | 1150.000000 | 1149.000000 | 0.0012595 | -1.202387 | 9.7864080 |
| valence | 3 | 1151 | 4.590755 | 0.7138819 | 4.43837 | 4.538283 | 0.8533066 | 3.665626 | 7.062879 | 3.397253 | 0.5017648 | -0.824026 | 0.0210421 |
Plots
#By Order
image_ratings_DITL_long %>%
ggplot(aes(x = row_id, y = value, col = rating)) +
geom_point(aes(color = rating)) +
geom_line(aes(color = rating)) +
ggtitle("Deep Affect Module: By Order") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))#By Order with subset highlighted
image_ratings_DITL %>%
ggplot(aes(x=row_id)) +
geom_rect(xmin = 375, xmax = 1000, ymin = 0, ymax= 7,
fill = "grey90", alpha = 0.03) +
geom_line(aes(y=valence), color = "blue", alpha = 0.5) +
geom_line(aes(y=arousal), color = "red", alpha = 0.75) +
ggtitle("Deep Affect Module: By Order") +
scale_y_continuous(name = "Score") +
scale_x_continuous(name = "Screenshot", n.breaks = 10) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"),
text = element_text(size = 16))